diff --git a/src/data/roadmaps/prompt-engineering/content/agents@Pw5LWA9vNRY0N2M0FW16f.md b/src/data/roadmaps/prompt-engineering/content/agents@Pw5LWA9vNRY0N2M0FW16f.md
index e2f83299f..4528ac857 100644
--- a/src/data/roadmaps/prompt-engineering/content/agents@Pw5LWA9vNRY0N2M0FW16f.md
+++ b/src/data/roadmaps/prompt-engineering/content/agents@Pw5LWA9vNRY0N2M0FW16f.md
@@ -1 +1,3 @@
-# Agents
\ No newline at end of file
+# Agents
+
+AI agents are autonomous systems that use LLMs to reason, plan, and take actions to achieve specific goals. They combine language understanding with tool usage, memory, and decision-making to perform complex, multi-step tasks. Agents can interact with external APIs and services while maintaining context across interactions.
\ No newline at end of file
diff --git a/src/data/roadmaps/prompt-engineering/content/ai-red-teaming@Wvu9Q_kNhH1_JlOgxAjP6.md b/src/data/roadmaps/prompt-engineering/content/ai-red-teaming@Wvu9Q_kNhH1_JlOgxAjP6.md
index 5fda0cdfe..90c53897f 100644
--- a/src/data/roadmaps/prompt-engineering/content/ai-red-teaming@Wvu9Q_kNhH1_JlOgxAjP6.md
+++ b/src/data/roadmaps/prompt-engineering/content/ai-red-teaming@Wvu9Q_kNhH1_JlOgxAjP6.md
@@ -1 +1,3 @@
-# AI Red Teaming
\ No newline at end of file
+# AI Red Teaming
+
+AI red teaming involves deliberately testing AI systems to find vulnerabilities, biases, or harmful behaviors through adversarial prompting. Teams attempt to make models produce undesired outputs, bypass safety measures, or exhibit problematic behaviors. This process helps identify weaknesses and improve AI safety and robustness before deployment.
\ No newline at end of file
diff --git a/src/data/roadmaps/prompt-engineering/content/ai-vs-agi@Sj1CMZzZp8kF-LuHcd_UU.md b/src/data/roadmaps/prompt-engineering/content/ai-vs-agi@Sj1CMZzZp8kF-LuHcd_UU.md
index 7f97e9943..1702891ac 100644
--- a/src/data/roadmaps/prompt-engineering/content/ai-vs-agi@Sj1CMZzZp8kF-LuHcd_UU.md
+++ b/src/data/roadmaps/prompt-engineering/content/ai-vs-agi@Sj1CMZzZp8kF-LuHcd_UU.md
@@ -1 +1,3 @@
-# AI vs AGI
\ No newline at end of file
+# AI vs AGI
+
+AI (Artificial Intelligence) refers to systems that perform specific tasks intelligently, while AGI (Artificial General Intelligence) represents hypothetical AI with human-level reasoning across all domains. Current LLMs are narrow AI - powerful at language tasks but lacking true understanding or general intelligence like AGI would possess.
\ No newline at end of file
diff --git a/src/data/roadmaps/prompt-engineering/content/anthropic@V8pDOwrRKKcHBTd4qlSsH.md b/src/data/roadmaps/prompt-engineering/content/anthropic@V8pDOwrRKKcHBTd4qlSsH.md
index 895ae718c..535909af5 100644
--- a/src/data/roadmaps/prompt-engineering/content/anthropic@V8pDOwrRKKcHBTd4qlSsH.md
+++ b/src/data/roadmaps/prompt-engineering/content/anthropic@V8pDOwrRKKcHBTd4qlSsH.md
@@ -1 +1,3 @@
-# Anthropic
\ No newline at end of file
+# Anthropic
+
+Anthropic created Claude, a family of large language models known for safety features and constitutional AI training. Claude models excel at following instructions, maintaining context, and avoiding harmful outputs. Their strong instruction-following capabilities and built-in safety measures make them valuable for reliable, ethical AI applications.
\ No newline at end of file
diff --git a/src/data/roadmaps/prompt-engineering/content/automatic-prompt-engineering@diHNCiuKHeMVgvJ4OMwVh.md b/src/data/roadmaps/prompt-engineering/content/automatic-prompt-engineering@diHNCiuKHeMVgvJ4OMwVh.md
index e675da613..98c5c5485 100644
--- a/src/data/roadmaps/prompt-engineering/content/automatic-prompt-engineering@diHNCiuKHeMVgvJ4OMwVh.md
+++ b/src/data/roadmaps/prompt-engineering/content/automatic-prompt-engineering@diHNCiuKHeMVgvJ4OMwVh.md
@@ -1 +1,3 @@
-# Automatic Prompt Engineering
\ No newline at end of file
+# Automatic Prompt Engineering
+
+Automatic Prompt Engineering (APE) uses LLMs to generate and optimize prompts automatically, reducing human effort while enhancing model performance. The process involves prompting a model to create multiple prompt variants, evaluating them using metrics like BLEU or ROUGE, then selecting the highest-scoring candidate. For example, generating 10 variants of customer order phrases for chatbot training, then testing and refining the best performers. This iterative approach helps discover effective prompts that humans might not consider, automating the optimization process.
\ No newline at end of file
diff --git a/src/data/roadmaps/prompt-engineering/content/calibrating-llms@P5nDyQbME53DOEfSkcY6I.md b/src/data/roadmaps/prompt-engineering/content/calibrating-llms@P5nDyQbME53DOEfSkcY6I.md
index 1d546c665..60dc6d3f0 100644
--- a/src/data/roadmaps/prompt-engineering/content/calibrating-llms@P5nDyQbME53DOEfSkcY6I.md
+++ b/src/data/roadmaps/prompt-engineering/content/calibrating-llms@P5nDyQbME53DOEfSkcY6I.md
@@ -1 +1,3 @@
-# Calibrating LLMs
\ No newline at end of file
+# Calibrating LLMs
+
+Calibrating LLMs involves adjusting models so their confidence scores accurately reflect their actual accuracy. Well-calibrated models express appropriate uncertainty - being confident when correct and uncertain when likely wrong. This helps users better trust and interpret model outputs, especially in critical applications where uncertainty awareness is crucial.
\ No newline at end of file
diff --git a/src/data/roadmaps/prompt-engineering/content/chain-of-thought-cot-prompting@weRaJxEplhKDyFWSMeoyI.md b/src/data/roadmaps/prompt-engineering/content/chain-of-thought-cot-prompting@weRaJxEplhKDyFWSMeoyI.md
index f8ac8ad8c..8fdcbb4ca 100644
--- a/src/data/roadmaps/prompt-engineering/content/chain-of-thought-cot-prompting@weRaJxEplhKDyFWSMeoyI.md
+++ b/src/data/roadmaps/prompt-engineering/content/chain-of-thought-cot-prompting@weRaJxEplhKDyFWSMeoyI.md
@@ -1 +1,3 @@
-# Chain of Thought (CoT) Prompting
\ No newline at end of file
+# Chain of Thought (CoT) Prompting
+
+Chain of Thought prompting improves LLM reasoning by generating intermediate reasoning steps before providing the final answer. Instead of jumping to conclusions, the model "thinks through" problems step by step. Simply adding "Let's think step by step" to prompts often dramatically improves accuracy on complex reasoning tasks and mathematical problems.
\ No newline at end of file
diff --git a/src/data/roadmaps/prompt-engineering/content/context-window@b-Xtkv6rt8QgzJXSShOX-.md b/src/data/roadmaps/prompt-engineering/content/context-window@b-Xtkv6rt8QgzJXSShOX-.md
index d2dbf8ba6..ecc324205 100644
--- a/src/data/roadmaps/prompt-engineering/content/context-window@b-Xtkv6rt8QgzJXSShOX-.md
+++ b/src/data/roadmaps/prompt-engineering/content/context-window@b-Xtkv6rt8QgzJXSShOX-.md
@@ -1 +1,3 @@
-# Context Window
\ No newline at end of file
+# Context Window
+
+Context window refers to the maximum number of tokens an LLM can process in a single interaction, including both input prompt and generated output. When exceeded, older parts are truncated. Understanding this constraint is crucial for prompt engineering—you must balance providing sufficient context with staying within token limits.
\ No newline at end of file
diff --git a/src/data/roadmaps/prompt-engineering/content/contextual-prompting@5TNK1KcSzh9GTKiEJnM-y.md b/src/data/roadmaps/prompt-engineering/content/contextual-prompting@5TNK1KcSzh9GTKiEJnM-y.md
index a33ef1905..1ca6538d8 100644
--- a/src/data/roadmaps/prompt-engineering/content/contextual-prompting@5TNK1KcSzh9GTKiEJnM-y.md
+++ b/src/data/roadmaps/prompt-engineering/content/contextual-prompting@5TNK1KcSzh9GTKiEJnM-y.md
@@ -1 +1,3 @@
-# Contextual Prompting
\ No newline at end of file
+# Contextual Prompting
+
+Contextual prompting provides specific background information or situational details relevant to the current task, helping LLMs understand nuances and tailor responses accordingly. Unlike system or role prompts, contextual prompts supply immediate, task-specific information that's dynamic and changes based on the situation. For example: "Context: You are writing for a blog about retro 80's arcade video games. Suggest 3 topics to write articles about." This technique ensures responses are relevant, accurate, and appropriately framed for the specific context provided.
\ No newline at end of file
diff --git a/src/data/roadmaps/prompt-engineering/content/fine-tuning-vs-prompt-engg@Ke5GT163k_ek9SzbcbBGE.md b/src/data/roadmaps/prompt-engineering/content/fine-tuning-vs-prompt-engg@Ke5GT163k_ek9SzbcbBGE.md
index 9ea09cdc2..f4f4c7993 100644
--- a/src/data/roadmaps/prompt-engineering/content/fine-tuning-vs-prompt-engg@Ke5GT163k_ek9SzbcbBGE.md
+++ b/src/data/roadmaps/prompt-engineering/content/fine-tuning-vs-prompt-engg@Ke5GT163k_ek9SzbcbBGE.md
@@ -1 +1,3 @@
-# Fine-Tuning vs Prompt Engg.
\ No newline at end of file
+# Fine-tuning vs Prompt Engineering
+
+Fine-tuning trains models on specific data to specialize behavior, while prompt engineering achieves customization through input design without model modification. Prompt engineering is faster, cheaper, and more accessible. Fine-tuning offers deeper customization but requires significant resources and expertise.
\ No newline at end of file
diff --git a/src/data/roadmaps/prompt-engineering/content/frequency-penalty@YIVNjkmTOY61VmL0md9Pj.md b/src/data/roadmaps/prompt-engineering/content/frequency-penalty@YIVNjkmTOY61VmL0md9Pj.md
index 149b0e10f..25dff903b 100644
--- a/src/data/roadmaps/prompt-engineering/content/frequency-penalty@YIVNjkmTOY61VmL0md9Pj.md
+++ b/src/data/roadmaps/prompt-engineering/content/frequency-penalty@YIVNjkmTOY61VmL0md9Pj.md
@@ -1 +1,3 @@
-# Frequency Penalty
\ No newline at end of file
+# Frequency Penalty
+
+Frequency penalty reduces token probability based on how frequently they've appeared in the text, with higher penalties for more frequent tokens. This prevents excessive repetition and encourages varied language use. The penalty scales with usage frequency, making overused words less likely to be selected again, improving content diversity.
\ No newline at end of file
diff --git a/src/data/roadmaps/prompt-engineering/content/google@o-6UKLZ6oCRbAKgRjH2uI.md b/src/data/roadmaps/prompt-engineering/content/google@o-6UKLZ6oCRbAKgRjH2uI.md
index fd0d223e1..bc8309390 100644
--- a/src/data/roadmaps/prompt-engineering/content/google@o-6UKLZ6oCRbAKgRjH2uI.md
+++ b/src/data/roadmaps/prompt-engineering/content/google@o-6UKLZ6oCRbAKgRjH2uI.md
@@ -1 +1,3 @@
-# Google
\ No newline at end of file
+# Google
+
+Google develops influential LLMs including Gemini, PaLM, and Bard. Through Vertex AI and Google Cloud Platform, they provide enterprise-grade model access with extensive prompt testing via Vertex AI Studio. Google's research has advanced many prompt engineering techniques, including Chain of Thought reasoning methods.
\ No newline at end of file
diff --git a/src/data/roadmaps/prompt-engineering/content/hallucination@SWDa3Su3VS815WQbvvNsa.md b/src/data/roadmaps/prompt-engineering/content/hallucination@SWDa3Su3VS815WQbvvNsa.md
index 0d348c082..d729ca648 100644
--- a/src/data/roadmaps/prompt-engineering/content/hallucination@SWDa3Su3VS815WQbvvNsa.md
+++ b/src/data/roadmaps/prompt-engineering/content/hallucination@SWDa3Su3VS815WQbvvNsa.md
@@ -1 +1,3 @@
-# Hallucination
\ No newline at end of file
+# Hallucination
+
+Hallucination in LLMs refers to generating plausible-sounding but factually incorrect or fabricated information. This occurs when models fill knowledge gaps or present uncertain information with apparent certainty. Mitigation techniques include requesting sources, asking for confidence levels, providing context, and always verifying critical information independently.
diff --git a/src/data/roadmaps/prompt-engineering/content/introduction@jrH1qE6EnFXL4fTyYU8gR.md b/src/data/roadmaps/prompt-engineering/content/introduction@jrH1qE6EnFXL4fTyYU8gR.md
index f6ecaa676..d3a3150d1 100644
--- a/src/data/roadmaps/prompt-engineering/content/introduction@jrH1qE6EnFXL4fTyYU8gR.md
+++ b/src/data/roadmaps/prompt-engineering/content/introduction@jrH1qE6EnFXL4fTyYU8gR.md
@@ -1 +1,3 @@
-# Introduction
\ No newline at end of file
+# Introduction
+
+Prompt engineering is the practice of designing effective inputs for Large Language Models to achieve desired outputs. This roadmap covers fundamental concepts, core techniques, model parameters, and advanced methods. It's a universal skill accessible to anyone, requiring no programming background, yet crucial for unlocking AI potential across diverse applications and domains.
\ No newline at end of file
diff --git a/src/data/roadmaps/prompt-engineering/content/llm-self-evaluation@CvV3GIvQhsTvE-TQjTpIQ.md b/src/data/roadmaps/prompt-engineering/content/llm-self-evaluation@CvV3GIvQhsTvE-TQjTpIQ.md
index 210fbacbd..9e0ef6b4d 100644
--- a/src/data/roadmaps/prompt-engineering/content/llm-self-evaluation@CvV3GIvQhsTvE-TQjTpIQ.md
+++ b/src/data/roadmaps/prompt-engineering/content/llm-self-evaluation@CvV3GIvQhsTvE-TQjTpIQ.md
@@ -1 +1,3 @@
-# LLM Self Evaluation
\ No newline at end of file
+# LLM Self Evaluation
+
+LLM self-evaluation involves prompting models to assess their own outputs for quality, accuracy, or adherence to criteria. This technique can identify errors, rate confidence levels, or check if responses meet specific requirements. Self-evaluation helps improve output quality through iterative refinement and provides valuable feedback for prompt optimization.
\ No newline at end of file
diff --git a/src/data/roadmaps/prompt-engineering/content/llm@pamV5Z8DRKk2ioZbg6QVK.md b/src/data/roadmaps/prompt-engineering/content/llm@pamV5Z8DRKk2ioZbg6QVK.md
index 13ae8d124..bed8df2a8 100644
--- a/src/data/roadmaps/prompt-engineering/content/llm@pamV5Z8DRKk2ioZbg6QVK.md
+++ b/src/data/roadmaps/prompt-engineering/content/llm@pamV5Z8DRKk2ioZbg6QVK.md
@@ -1 +1,3 @@
-# LLM
\ No newline at end of file
+# LLM
+
+Large Language Models (LLMs) are AI systems trained on vast text data to understand and generate human-like language. They work as prediction engines, analyzing input and predicting the next most likely token. LLMs perform tasks like text generation, translation, summarization, and Q&A. Understanding token processing is key to effective prompt engineering.
\ No newline at end of file
diff --git a/src/data/roadmaps/prompt-engineering/content/llms-and-how-they-work@74JxgfJ_1qmVNZ_QRp9Ne.md b/src/data/roadmaps/prompt-engineering/content/llms-and-how-they-work@74JxgfJ_1qmVNZ_QRp9Ne.md
index f5e38292f..5c306d74a 100644
--- a/src/data/roadmaps/prompt-engineering/content/llms-and-how-they-work@74JxgfJ_1qmVNZ_QRp9Ne.md
+++ b/src/data/roadmaps/prompt-engineering/content/llms-and-how-they-work@74JxgfJ_1qmVNZ_QRp9Ne.md
@@ -1 +1,3 @@
-# LLMs and how they work?
\ No newline at end of file
+# LLMs and How They Work
+
+LLMs function as sophisticated prediction engines that process text sequentially, predicting the next token based on relationships between previous tokens and patterns from training data. They don't predict single tokens directly but generate probability distributions over possible next tokens, which are then sampled using parameters like temperature and top-K. The model repeatedly adds predicted tokens to the sequence, building responses iteratively. This token-by-token prediction process, combined with massive training datasets, enables LLMs to generate coherent, contextually relevant text across diverse applications and domains.
\ No newline at end of file
diff --git a/src/data/roadmaps/prompt-engineering/content/max-tokens@vK9Gf8dGu2UvvJJhhuHG9.md b/src/data/roadmaps/prompt-engineering/content/max-tokens@vK9Gf8dGu2UvvJJhhuHG9.md
index ca9ce5d9a..1fc810c5d 100644
--- a/src/data/roadmaps/prompt-engineering/content/max-tokens@vK9Gf8dGu2UvvJJhhuHG9.md
+++ b/src/data/roadmaps/prompt-engineering/content/max-tokens@vK9Gf8dGu2UvvJJhhuHG9.md
@@ -1 +1,3 @@
-# Max Tokens
\ No newline at end of file
+# Max Tokens
+
+Max tokens setting controls the maximum number of tokens an LLM can generate in response, directly impacting computation cost, response time, and energy consumption. Setting lower limits doesn't make models more concise—it simply stops generation when the limit is reached. This parameter is crucial for techniques like ReAct where models might generate unnecessary tokens after the desired response. Balancing max tokens involves considering cost efficiency, response completeness, and application requirements while ensuring critical information isn't truncated.
\ No newline at end of file
diff --git a/src/data/roadmaps/prompt-engineering/content/meta@Td2YzDFT4LPGDw8JMmQSQ.md b/src/data/roadmaps/prompt-engineering/content/meta@Td2YzDFT4LPGDw8JMmQSQ.md
index dabc8eee5..497572fcb 100644
--- a/src/data/roadmaps/prompt-engineering/content/meta@Td2YzDFT4LPGDw8JMmQSQ.md
+++ b/src/data/roadmaps/prompt-engineering/content/meta@Td2YzDFT4LPGDw8JMmQSQ.md
@@ -1 +1,3 @@
-# Meta
\ No newline at end of file
+# Meta
+
+Meta (formerly Facebook) develops the Llama family of open-source large language models. Llama models are available for research and commercial use, offering strong performance across various tasks. For prompt engineering, Meta's models provide transparency in training data and architecture, allowing developers to fine-tune and customize prompts for specific applications without vendor lock-in.
\ No newline at end of file
diff --git a/src/data/roadmaps/prompt-engineering/content/model-weights--parameters@yfsjW1eze8mWT0iHxv078.md b/src/data/roadmaps/prompt-engineering/content/model-weights--parameters@yfsjW1eze8mWT0iHxv078.md
index 8df7a72ae..4d7b6557c 100644
--- a/src/data/roadmaps/prompt-engineering/content/model-weights--parameters@yfsjW1eze8mWT0iHxv078.md
+++ b/src/data/roadmaps/prompt-engineering/content/model-weights--parameters@yfsjW1eze8mWT0iHxv078.md
@@ -1 +1,3 @@
-# Model Weights / Parameters
\ No newline at end of file
+# Model Weights / Parameters
+
+Model weights and parameters are the learned values that define an LLM's behavior and knowledge. Parameters are the trainable variables adjusted during training, while weights represent their final values. Understanding parameter count helps gauge model capabilities - larger models typically have more parameters and better performance but require more computational resources.
\ No newline at end of file
diff --git a/src/data/roadmaps/prompt-engineering/content/one-shot--few-shot-prompting@Iufv_LsgUNls-Alx_Btlh.md b/src/data/roadmaps/prompt-engineering/content/one-shot--few-shot-prompting@Iufv_LsgUNls-Alx_Btlh.md
index 2c88ee178..191e9bc5a 100644
--- a/src/data/roadmaps/prompt-engineering/content/one-shot--few-shot-prompting@Iufv_LsgUNls-Alx_Btlh.md
+++ b/src/data/roadmaps/prompt-engineering/content/one-shot--few-shot-prompting@Iufv_LsgUNls-Alx_Btlh.md
@@ -1 +1,3 @@
-# One-Shot / Few-Shot Prompting
\ No newline at end of file
+# One-Shot & Few-Shot Prompting
+
+One-shot provides a single example to guide model behavior, while few-shot includes multiple examples (3-5) to demonstrate desired patterns. Examples show output structure, style, and tone, increasing accuracy and consistency. Use few-shot for complex formatting, specialized tasks, and when zero-shot results are inconsistent.
\ No newline at end of file
diff --git a/src/data/roadmaps/prompt-engineering/content/openai@Yb5cQiV2ETxPbBYCLOpt2.md b/src/data/roadmaps/prompt-engineering/content/openai@Yb5cQiV2ETxPbBYCLOpt2.md
index 9357a775f..661b82428 100644
--- a/src/data/roadmaps/prompt-engineering/content/openai@Yb5cQiV2ETxPbBYCLOpt2.md
+++ b/src/data/roadmaps/prompt-engineering/content/openai@Yb5cQiV2ETxPbBYCLOpt2.md
@@ -1 +1,3 @@
-# OpenAI
\ No newline at end of file
+# OpenAI
+
+OpenAI developed influential language models including GPT-3, GPT-4, and ChatGPT, setting industry standards for prompt engineering practices. Their API provides access to powerful LLMs with configurable parameters like temperature and max tokens. Many prompt engineering techniques and best practices originated from working with OpenAI systems.
\ No newline at end of file
diff --git a/src/data/roadmaps/prompt-engineering/content/output-control@wSf7Zr8ZYBuKWX0GQX6J3.md b/src/data/roadmaps/prompt-engineering/content/output-control@wSf7Zr8ZYBuKWX0GQX6J3.md
index e74592553..505d9c454 100644
--- a/src/data/roadmaps/prompt-engineering/content/output-control@wSf7Zr8ZYBuKWX0GQX6J3.md
+++ b/src/data/roadmaps/prompt-engineering/content/output-control@wSf7Zr8ZYBuKWX0GQX6J3.md
@@ -1 +1,3 @@
-# Output Control
\ No newline at end of file
+# Output Control
+
+Output control encompasses techniques and parameters for managing LLM response characteristics including length, format, style, and content boundaries. Key methods include max tokens for length limits, stop sequences for precise boundaries, temperature for creativity control, and structured output requirements for format consistency. Effective output control combines prompt engineering techniques with model parameters to ensure responses meet specific requirements. This is crucial for production applications where consistent, appropriately formatted outputs are essential for user experience and system integration.
\ No newline at end of file
diff --git a/src/data/roadmaps/prompt-engineering/content/presence-penalty@WpO8V5caudySVehOcuDvK.md b/src/data/roadmaps/prompt-engineering/content/presence-penalty@WpO8V5caudySVehOcuDvK.md
index 0a9e0c807..f118cda26 100644
--- a/src/data/roadmaps/prompt-engineering/content/presence-penalty@WpO8V5caudySVehOcuDvK.md
+++ b/src/data/roadmaps/prompt-engineering/content/presence-penalty@WpO8V5caudySVehOcuDvK.md
@@ -1 +1,3 @@
-# Presence Penalty
\ No newline at end of file
+# Presence Penalty
+
+Presence penalty reduces the likelihood of repeating tokens that have already appeared in the text, encouraging diverse vocabulary usage. Unlike frequency penalty which considers how often tokens appear, presence penalty applies the same penalty to any previously used token, promoting varied content and creativity.
\ No newline at end of file
diff --git a/src/data/roadmaps/prompt-engineering/content/prompt-debiasing@0H2keZYD8iTNyBgmNVhto.md b/src/data/roadmaps/prompt-engineering/content/prompt-debiasing@0H2keZYD8iTNyBgmNVhto.md
index 445b509cf..b7b71a717 100644
--- a/src/data/roadmaps/prompt-engineering/content/prompt-debiasing@0H2keZYD8iTNyBgmNVhto.md
+++ b/src/data/roadmaps/prompt-engineering/content/prompt-debiasing@0H2keZYD8iTNyBgmNVhto.md
@@ -1 +1,3 @@
-# Prompt Debiasing
\ No newline at end of file
+# Prompt Debiasing
+
+Prompt debiasing involves techniques to reduce unwanted biases in LLM outputs by carefully crafting prompts. This includes using neutral language, diverse examples, and explicit instructions to avoid stereotypes or unfair representations. Effective debiasing helps ensure AI outputs are more fair, inclusive, and representative across different groups and perspectives.
\ No newline at end of file
diff --git a/src/data/roadmaps/prompt-engineering/content/prompt-ensembling@HOqWHqAkxLX8f2ImSmZE7.md b/src/data/roadmaps/prompt-engineering/content/prompt-ensembling@HOqWHqAkxLX8f2ImSmZE7.md
index 1012c651a..e9ea14eab 100644
--- a/src/data/roadmaps/prompt-engineering/content/prompt-ensembling@HOqWHqAkxLX8f2ImSmZE7.md
+++ b/src/data/roadmaps/prompt-engineering/content/prompt-ensembling@HOqWHqAkxLX8f2ImSmZE7.md
@@ -1 +1,3 @@
-# Prompt Ensembling
\ No newline at end of file
+# Prompt Ensembling
+
+Prompt ensembling combines multiple different prompts or prompt variations to improve output quality and consistency. This technique involves running the same query with different prompt formulations and aggregating results through voting, averaging, or selection. Ensembling reduces variance and increases reliability by leveraging diverse prompt perspectives.
\ No newline at end of file
diff --git a/src/data/roadmaps/prompt-engineering/content/prompt-injection@6W_ONYREbXHwPigoDx1cW.md b/src/data/roadmaps/prompt-engineering/content/prompt-injection@6W_ONYREbXHwPigoDx1cW.md
index 7227af039..d0ddc8250 100644
--- a/src/data/roadmaps/prompt-engineering/content/prompt-injection@6W_ONYREbXHwPigoDx1cW.md
+++ b/src/data/roadmaps/prompt-engineering/content/prompt-injection@6W_ONYREbXHwPigoDx1cW.md
@@ -1 +1,3 @@
-# Prompt Injection
\ No newline at end of file
+# Prompt Injection
+
+Prompt injection is a security vulnerability where malicious users manipulate LLM inputs to override intended behavior, bypass safety measures, or extract sensitive information. Attackers embed instructions within data to make models ignore original prompts and follow malicious commands. Mitigation requires input sanitization, injection-resistant prompt design, and proper security boundaries.
\ No newline at end of file
diff --git a/src/data/roadmaps/prompt-engineering/content/rag@gxydtFKmnXNY9I5kpTwjP.md b/src/data/roadmaps/prompt-engineering/content/rag@gxydtFKmnXNY9I5kpTwjP.md
index ee0679087..4e0e070ab 100644
--- a/src/data/roadmaps/prompt-engineering/content/rag@gxydtFKmnXNY9I5kpTwjP.md
+++ b/src/data/roadmaps/prompt-engineering/content/rag@gxydtFKmnXNY9I5kpTwjP.md
@@ -1 +1,3 @@
-# RAG
\ No newline at end of file
+# RAG
+
+Retrieval-Augmented Generation (RAG) combines LLMs with external knowledge retrieval to ground responses in verified, current information. RAG retrieves relevant documents before generating responses, reducing hallucinations and enabling access to information beyond the model's training cutoff. This approach improves accuracy and provides source attribution.
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diff --git a/src/data/roadmaps/prompt-engineering/content/react-prompting@8Ks6txRSUfMK7VotSQ4sC.md b/src/data/roadmaps/prompt-engineering/content/react-prompting@8Ks6txRSUfMK7VotSQ4sC.md
index 12743c9f0..e620e1a89 100644
--- a/src/data/roadmaps/prompt-engineering/content/react-prompting@8Ks6txRSUfMK7VotSQ4sC.md
+++ b/src/data/roadmaps/prompt-engineering/content/react-prompting@8Ks6txRSUfMK7VotSQ4sC.md
@@ -1 +1,3 @@
-# ReAct Prompting
\ No newline at end of file
+# ReAct Prompting
+
+ReAct (Reason and Act) prompting enables LLMs to solve complex tasks by combining reasoning with external tool interactions. It follows a thought-action-observation loop: analyze the problem, perform actions using external APIs, review results, and iterate until solved. Useful for research, multi-step problems, and tasks requiring current data.
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diff --git a/src/data/roadmaps/prompt-engineering/content/repetition-penalties@g8ylIg4Zh567u-E3yVVY4.md b/src/data/roadmaps/prompt-engineering/content/repetition-penalties@g8ylIg4Zh567u-E3yVVY4.md
index 08df26272..b7d46b613 100644
--- a/src/data/roadmaps/prompt-engineering/content/repetition-penalties@g8ylIg4Zh567u-E3yVVY4.md
+++ b/src/data/roadmaps/prompt-engineering/content/repetition-penalties@g8ylIg4Zh567u-E3yVVY4.md
@@ -1 +1,3 @@
-# Repetition Penalties
\ No newline at end of file
+# Repetition Penalties
+
+Repetition penalties discourage LLMs from repeating words or phrases by reducing the probability of selecting previously used tokens. This includes frequency penalty (scales with usage count) and presence penalty (applies equally to any used token). These parameters improve output quality by promoting vocabulary diversity and preventing redundant phrasing.
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diff --git a/src/data/roadmaps/prompt-engineering/content/role-prompting@XHWKGaSRBYT4MsCHwV-iR.md b/src/data/roadmaps/prompt-engineering/content/role-prompting@XHWKGaSRBYT4MsCHwV-iR.md
index c833363f9..40b5a5732 100644
--- a/src/data/roadmaps/prompt-engineering/content/role-prompting@XHWKGaSRBYT4MsCHwV-iR.md
+++ b/src/data/roadmaps/prompt-engineering/content/role-prompting@XHWKGaSRBYT4MsCHwV-iR.md
@@ -1 +1,3 @@
-# Role Prompting
\ No newline at end of file
+# Role Prompting
+
+Role prompting assigns a specific character, identity, or professional role to the LLM to generate responses consistent with that role's expertise, personality, and communication style. By establishing roles like "teacher," "travel guide," or "software engineer," you provide the model with appropriate domain knowledge, perspective, and tone for more targeted, natural interactions.
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diff --git a/src/data/roadmaps/prompt-engineering/content/sampling-parameters@JgigM7HvmNOuKnp60v1Ce.md b/src/data/roadmaps/prompt-engineering/content/sampling-parameters@JgigM7HvmNOuKnp60v1Ce.md
index aa4801b74..3ac47233a 100644
--- a/src/data/roadmaps/prompt-engineering/content/sampling-parameters@JgigM7HvmNOuKnp60v1Ce.md
+++ b/src/data/roadmaps/prompt-engineering/content/sampling-parameters@JgigM7HvmNOuKnp60v1Ce.md
@@ -1 +1,3 @@
-# Sampling Parameters
\ No newline at end of file
+# Sampling Parameters
+
+Sampling parameters (temperature, top-K, top-P) control how LLMs select tokens from probability distributions, determining output randomness and creativity. These parameters interact: at extreme settings, one can override others (temperature 0 makes top-K/top-P irrelevant). A balanced starting point is temperature 0.2, top-P 0.95, top-K 30 for coherent but creative results. Understanding their interactions is crucial for optimal prompting—use temperature 0 for factual tasks, higher values for creativity, and combine settings strategically based on your specific use case.
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diff --git a/src/data/roadmaps/prompt-engineering/content/self-consistency-prompting@1EzqCoplXPiHjp9Z-vqn-.md b/src/data/roadmaps/prompt-engineering/content/self-consistency-prompting@1EzqCoplXPiHjp9Z-vqn-.md
index 3ca60a85d..f6174efee 100644
--- a/src/data/roadmaps/prompt-engineering/content/self-consistency-prompting@1EzqCoplXPiHjp9Z-vqn-.md
+++ b/src/data/roadmaps/prompt-engineering/content/self-consistency-prompting@1EzqCoplXPiHjp9Z-vqn-.md
@@ -1 +1,3 @@
-# Self-Consistency Prompting
\ No newline at end of file
+# Self-Consistency Prompting
+
+Self-consistency prompting generates multiple reasoning paths for the same problem using higher temperature settings, then selects the most commonly occurring answer through majority voting. This technique combines sampling and voting to improve accuracy and provides pseudo-probability of answer correctness. While more expensive due to multiple API calls, it significantly enhances reliability for complex reasoning tasks by reducing the impact of single incorrect reasoning chains and leveraging diverse problem-solving approaches.
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diff --git a/src/data/roadmaps/prompt-engineering/content/step-back-prompting@2MboHh8ugkoH8dSd9d4Mk.md b/src/data/roadmaps/prompt-engineering/content/step-back-prompting@2MboHh8ugkoH8dSd9d4Mk.md
index a786a6952..5e92ea3aa 100644
--- a/src/data/roadmaps/prompt-engineering/content/step-back-prompting@2MboHh8ugkoH8dSd9d4Mk.md
+++ b/src/data/roadmaps/prompt-engineering/content/step-back-prompting@2MboHh8ugkoH8dSd9d4Mk.md
@@ -1 +1,3 @@
-# Step-back Prompting
\ No newline at end of file
+# Step-Back Prompting
+
+Step-back prompting improves LLM performance by first asking a general question related to the specific task, then using that answer to inform the final response. This technique activates relevant background knowledge before attempting the specific problem. For example, before writing a video game level storyline, first ask "What are key settings for engaging first-person shooter levels?" then use those insights to create the specific storyline. This approach reduces biases and improves accuracy by grounding responses in broader principles.
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diff --git a/src/data/roadmaps/prompt-engineering/content/stop-sequences@v3CylRlojeltcwnE76j8Q.md b/src/data/roadmaps/prompt-engineering/content/stop-sequences@v3CylRlojeltcwnE76j8Q.md
index cf61ab5a3..160620bf5 100644
--- a/src/data/roadmaps/prompt-engineering/content/stop-sequences@v3CylRlojeltcwnE76j8Q.md
+++ b/src/data/roadmaps/prompt-engineering/content/stop-sequences@v3CylRlojeltcwnE76j8Q.md
@@ -1 +1,3 @@
-# Stop Sequences
\ No newline at end of file
+# Stop Sequences
+
+Stop sequences are specific strings that signal the LLM to stop generating text when encountered, providing precise control over output length and format. Common examples include newlines, periods, or custom markers like "###" or "END". This parameter is particularly useful for structured outputs, preventing models from generating beyond intended boundaries. Stop sequences are essential for ReAct prompting and other scenarios where you need clean, precisely bounded responses. They offer more control than max tokens by stopping at logical breakpoints rather than arbitrary token limits.
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diff --git a/src/data/roadmaps/prompt-engineering/content/structured-outputs@j-PWO-ZmF9Oi9A5bwMRto.md b/src/data/roadmaps/prompt-engineering/content/structured-outputs@j-PWO-ZmF9Oi9A5bwMRto.md
index 90dd31221..133637094 100644
--- a/src/data/roadmaps/prompt-engineering/content/structured-outputs@j-PWO-ZmF9Oi9A5bwMRto.md
+++ b/src/data/roadmaps/prompt-engineering/content/structured-outputs@j-PWO-ZmF9Oi9A5bwMRto.md
@@ -1 +1,3 @@
-# Structured Outputs
\ No newline at end of file
+# Structured Outputs
+
+Structured outputs involve prompting LLMs to return responses in specific formats like JSON, XML, or other organized structures rather than free-form text. This approach forces models to organize information systematically, reduces hallucinations by imposing format constraints, enables easy programmatic processing, and facilitates integration with applications. For example, requesting movie classification results as JSON with specified schema ensures consistent, parseable responses. Structured outputs are particularly valuable for data extraction, API integration, and applications requiring reliable data formatting.
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diff --git a/src/data/roadmaps/prompt-engineering/content/system-prompting@fWo39-hehRgwmx7CF36mM.md b/src/data/roadmaps/prompt-engineering/content/system-prompting@fWo39-hehRgwmx7CF36mM.md
index a8f1b5205..ea6a83046 100644
--- a/src/data/roadmaps/prompt-engineering/content/system-prompting@fWo39-hehRgwmx7CF36mM.md
+++ b/src/data/roadmaps/prompt-engineering/content/system-prompting@fWo39-hehRgwmx7CF36mM.md
@@ -1 +1,3 @@
-# System Prompting
\ No newline at end of file
+# System Prompting
+
+System prompting sets the overall context, purpose, and operational guidelines for LLMs. It defines the model's role, behavioral constraints, output format requirements, and safety guardrails. System prompts provide foundational parameters that influence all subsequent interactions, ensuring consistent, controlled, and structured AI responses throughout the session.
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diff --git a/src/data/roadmaps/prompt-engineering/content/temperature@iMwg-I76-Tg5dhu8DGO6U.md b/src/data/roadmaps/prompt-engineering/content/temperature@iMwg-I76-Tg5dhu8DGO6U.md
index 9028fe2d0..e3fe8b882 100644
--- a/src/data/roadmaps/prompt-engineering/content/temperature@iMwg-I76-Tg5dhu8DGO6U.md
+++ b/src/data/roadmaps/prompt-engineering/content/temperature@iMwg-I76-Tg5dhu8DGO6U.md
@@ -1 +1,3 @@
-# Temperature
\ No newline at end of file
+# Temperature
+
+Temperature controls the randomness in token selection during text generation. Lower values (0-0.3) produce deterministic, factual outputs. Medium values (0.5-0.7) balance creativity and coherence. Higher values (0.8-1.0) generate creative, diverse outputs but may be less coherent. Use low temperature for math/facts, high for creative writing.
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diff --git a/src/data/roadmaps/prompt-engineering/content/tokens@NPcaSEteeEA5g22wQ7nL_.md b/src/data/roadmaps/prompt-engineering/content/tokens@NPcaSEteeEA5g22wQ7nL_.md
index 7e1c51cd3..acfe2f425 100644
--- a/src/data/roadmaps/prompt-engineering/content/tokens@NPcaSEteeEA5g22wQ7nL_.md
+++ b/src/data/roadmaps/prompt-engineering/content/tokens@NPcaSEteeEA5g22wQ7nL_.md
@@ -1 +1,3 @@
-# Tokens
\ No newline at end of file
+# Tokens
+
+Tokens are fundamental units of text that LLMs process, created by breaking down text into smaller components like words, subwords, or characters. Understanding tokens is crucial because models predict the next token in sequences, API costs are based on token count, and models have maximum token limits for input and output.
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diff --git a/src/data/roadmaps/prompt-engineering/content/top-k@FF8ai1v5GDzxXLQhpwuPj.md b/src/data/roadmaps/prompt-engineering/content/top-k@FF8ai1v5GDzxXLQhpwuPj.md
index 72cfa3ce7..4ddde7af3 100644
--- a/src/data/roadmaps/prompt-engineering/content/top-k@FF8ai1v5GDzxXLQhpwuPj.md
+++ b/src/data/roadmaps/prompt-engineering/content/top-k@FF8ai1v5GDzxXLQhpwuPj.md
@@ -1 +1,3 @@
-# Top-K
\ No newline at end of file
+# Top-K
+
+Top-K restricts token selection to the K most likely tokens from the probability distribution. Low values (1-10) produce conservative, factual outputs. Medium values (20-50) balance creativity and quality. High values (50+) enable diverse, creative outputs. Use low K for technical tasks, high K for creative writing.
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diff --git a/src/data/roadmaps/prompt-engineering/content/top-p@-G1U1jDN5st1fTUtQmMl1.md b/src/data/roadmaps/prompt-engineering/content/top-p@-G1U1jDN5st1fTUtQmMl1.md
index 4c3a6d7c9..7f0edff4e 100644
--- a/src/data/roadmaps/prompt-engineering/content/top-p@-G1U1jDN5st1fTUtQmMl1.md
+++ b/src/data/roadmaps/prompt-engineering/content/top-p@-G1U1jDN5st1fTUtQmMl1.md
@@ -1 +1,3 @@
-# Top-P
\ No newline at end of file
+# Top-P
+
+Top-P (nucleus sampling) selects tokens from the smallest set whose cumulative probability exceeds threshold P. Unlike Top-K's fixed number, Top-P dynamically adjusts based on probability distribution. Low values (0.1-0.5) produce focused outputs, medium (0.6-0.9) balance creativity and coherence, high (0.9-0.99) enable creative diversity.
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diff --git a/src/data/roadmaps/prompt-engineering/content/tree-of-thoughts-tot-prompting@ob9D0W9B9145Da64nbi1M.md b/src/data/roadmaps/prompt-engineering/content/tree-of-thoughts-tot-prompting@ob9D0W9B9145Da64nbi1M.md
index c4f4d214d..29b17f86b 100644
--- a/src/data/roadmaps/prompt-engineering/content/tree-of-thoughts-tot-prompting@ob9D0W9B9145Da64nbi1M.md
+++ b/src/data/roadmaps/prompt-engineering/content/tree-of-thoughts-tot-prompting@ob9D0W9B9145Da64nbi1M.md
@@ -1 +1,3 @@
-# Tree of Thoughts (ToT) Prompting
\ No newline at end of file
+# Tree of Thoughts (ToT) Prompting
+
+Tree of Thoughts (ToT) generalizes Chain of Thought by allowing LLMs to explore multiple reasoning paths simultaneously rather than following a single linear chain. This approach maintains a tree structure where each thought represents a coherent step toward solving a problem, enabling the model to branch out and explore different reasoning directions. ToT is particularly effective for complex tasks requiring exploration and is well-suited for problems that benefit from considering multiple solution approaches before converging on the best answer.
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diff --git a/src/data/roadmaps/prompt-engineering/content/what-is-a-prompt@i4ijY3T5gLgNz0XqRipXe.md b/src/data/roadmaps/prompt-engineering/content/what-is-a-prompt@i4ijY3T5gLgNz0XqRipXe.md
index 9b0abdf72..78304cb0f 100644
--- a/src/data/roadmaps/prompt-engineering/content/what-is-a-prompt@i4ijY3T5gLgNz0XqRipXe.md
+++ b/src/data/roadmaps/prompt-engineering/content/what-is-a-prompt@i4ijY3T5gLgNz0XqRipXe.md
@@ -1 +1,3 @@
-# What is a Prompt?
\ No newline at end of file
+# What is a Prompt?
+
+A prompt is an input provided to a Large Language Model (LLM) to generate a response or prediction. It serves as the instruction or context that guides the AI model's output generation process. Effective prompts are clear, specific, well-structured, and goal-oriented, directly affecting the accuracy and relevance of AI responses.
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diff --git a/src/data/roadmaps/prompt-engineering/content/what-is-prompt-engineering@43drPbTwPqJQPyzwYUdBT.md b/src/data/roadmaps/prompt-engineering/content/what-is-prompt-engineering@43drPbTwPqJQPyzwYUdBT.md
index 04d033aca..d4e2252b9 100644
--- a/src/data/roadmaps/prompt-engineering/content/what-is-prompt-engineering@43drPbTwPqJQPyzwYUdBT.md
+++ b/src/data/roadmaps/prompt-engineering/content/what-is-prompt-engineering@43drPbTwPqJQPyzwYUdBT.md
@@ -1 +1,3 @@
-# What is Prompt Engineering?
\ No newline at end of file
+# What is Prompt Engineering?
+
+Prompt engineering is the practice of crafting effective input text to guide AI language models toward desired outputs. It involves designing prompts that communicate intent clearly to get accurate, relevant responses. This iterative process requires understanding how LLMs work as prediction engines and using techniques to optimize their performance for specific tasks.
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diff --git a/src/data/roadmaps/prompt-engineering/content/xai@3wshuH7_DXgbhxsLzzI4D.md b/src/data/roadmaps/prompt-engineering/content/xai@3wshuH7_DXgbhxsLzzI4D.md
index 12fb209fc..dbeadae00 100644
--- a/src/data/roadmaps/prompt-engineering/content/xai@3wshuH7_DXgbhxsLzzI4D.md
+++ b/src/data/roadmaps/prompt-engineering/content/xai@3wshuH7_DXgbhxsLzzI4D.md
@@ -1 +1,3 @@
-# xAI
\ No newline at end of file
+# xAI
+
+xAI is Elon Musk's AI company that created Grok, a conversational AI model trained on web data with a focus on real-time information and humor. Grok aims to be more truthful and less politically correct than other models. For prompt engineering, xAI offers unique capabilities in accessing current events and generating responses with a distinctive conversational style.
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diff --git a/src/data/roadmaps/prompt-engineering/content/zero-shot-prompting@GRerL9UXN73TwpCW2eTIE.md b/src/data/roadmaps/prompt-engineering/content/zero-shot-prompting@GRerL9UXN73TwpCW2eTIE.md
index 65b4930d5..0d24c3290 100644
--- a/src/data/roadmaps/prompt-engineering/content/zero-shot-prompting@GRerL9UXN73TwpCW2eTIE.md
+++ b/src/data/roadmaps/prompt-engineering/content/zero-shot-prompting@GRerL9UXN73TwpCW2eTIE.md
@@ -1 +1,3 @@
-# Zero-Shot Prompting
\ No newline at end of file
+# Zero-Shot Prompting
+
+Zero-shot prompting provides only a task description without examples, relying on the model's training patterns. Simply describe the task clearly, provide input data, and optionally specify output format. Works well for simple classification, text generation, and Q&A, but may produce inconsistent results for complex tasks.
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