chore: sync content to repository - prompt-engineering

src/data/roadmaps/prompt-engineering/content/agents@Pw5LWA9vNRY0N2M0FW16f.md

@@ -5,4 +5,4 @@ AI agents are autonomous systems that use LLMs to reason, plan, and take actions
 Visit the following resources to learn more:
 
 - [@official@Tool use overview - Anthropic](https://platform.claude.com/docs/en/agents-and-tools/tool-use/overview)
-- [@article@Introduction to AI Agents - DAIR.AI](https://www.promptingguide.ai/agents/introduction)
+- [@article@Introduction to AI Agents - DAIR.AI](https://www.promptingguide.ai/agents/introduction)

src/data/roadmaps/prompt-engineering/content/ai-red-teaming@Wvu9Q_kNhH1_JlOgxAjP6.md

@@ -4,6 +4,4 @@ AI red teaming involves deliberately testing AI systems to find vulnerabilities,
 
 Visit the following resources to learn more:
 
-- [@official@Define success and build evaluations - Anthropic](https://platform.claude.com/docs/en/test-and-evaluate/develop-tests)
-- [@official@OWASP Top 10 for LLM Applications 2025](https://genai.owasp.org/llmrisk/)
-- [@opensource@Microsoft PyRIT - Risk Identification for GenAI](https://github.com/microsoft/PyRIT)
+- [@roadmap@Visit the Dedicated AI Red Teaming Roadmap](https://roadmap.sh/ai-red-teaming)

src/data/roadmaps/prompt-engineering/content/ai-vs-agi@Sj1CMZzZp8kF-LuHcd_UU.md

@@ -4,4 +4,4 @@ AI (Artificial Intelligence) refers to systems that perform specific tasks intel
 
 Visit the following resources to learn more:
 
-- [@article@Artificial general intelligence - Wikipedia](https://en.wikipedia.org/wiki/Artificial_general_intelligence)
+- [@article@Artificial general intelligence - Wikipedia](https://en.wikipedia.org/wiki/Artificial_general_intelligence)

src/data/roadmaps/prompt-engineering/content/anthropic@V8pDOwrRKKcHBTd4qlSsH.md

@@ -5,4 +5,4 @@ Anthropic develops Claude, a family of large language models focused on safety a
 Visit the following resources to learn more:
 
 - [@official@Claude API Documentation](https://docs.anthropic.com/en/docs/intro)
-- [@official@Anthropic Research](https://www.anthropic.com/research)
+- [@official@Anthropic Research](https://www.anthropic.com/research)

src/data/roadmaps/prompt-engineering/content/automatic-prompt-engineering@diHNCiuKHeMVgvJ4OMwVh.md

@@ -4,4 +4,4 @@ Automatic Prompt Engineering (APE) uses LLMs to generate and optimize prompts au
 
 Visit the following resources to learn more:
 
-- [@article@Automatic Prompt Engineer - DAIR.AI](https://www.promptingguide.ai/techniques/ape)
+- [@article@Automatic Prompt Engineer - DAIR.AI](https://www.promptingguide.ai/techniques/ape)

src/data/roadmaps/prompt-engineering/content/calibrating-llms@P5nDyQbME53DOEfSkcY6I.md

@@ -4,4 +4,4 @@ Calibrating LLMs involves adjusting models so their confidence scores accurately
 
 Visit the following resources to learn more:
 
-- [@article@Calibrating LLMs - LearnPrompting](https://learnprompting.org/docs/reliability/calibration)
+- [@article@Calibrating LLMs - LearnPrompting](https://learnprompting.org/docs/reliability/calibration)

src/data/roadmaps/prompt-engineering/content/chain-of-thought-cot-prompting@weRaJxEplhKDyFWSMeoyI.md

@@ -7,4 +7,4 @@ Visit the following resources to learn more:
 - [@article@Chain-of-Thought Prompting - DAIR.AI](https://www.promptingguide.ai/techniques/cot)
 - [@article@Chain-of-Thought Prompting - LearnPrompting](https://learnprompting.org/docs/intermediate/chain_of_thought)
 - [@article@Reasoning LLMs Guide - DAIR.AI](https://www.promptingguide.ai/guides/reasoning-llms)
-- [@video@Context Engineering vs. Prompt Engineering: Smarter AI with RAG & Agents](https://youtu.be/vD0E3EUb8-8?si=Y6MCLPzjmhMB4jSu&t=203)
+- [@video@Context Engineering vs. Prompt Engineering: Smarter AI with RAG & Agents](https://youtu.be/vD0E3EUb8-8?si=Y6MCLPzjmhMB4jSu&t=203)

src/data/roadmaps/prompt-engineering/content/context-window@b-Xtkv6rt8QgzJXSShOX-.md

@@ -5,4 +5,4 @@ Context window refers to the maximum number of tokens an LLM can process in a si
 Visit the following resources to learn more:
 
 - [@official@Context windows - Anthropic](https://platform.claude.com/docs/en/build-with-claude/context-windows)
-- [@article@What is a context window? - IBM](https://www.ibm.com/think/topics/context-window)
+- [@article@What is a context window? - IBM](https://www.ibm.com/think/topics/context-window)

src/data/roadmaps/prompt-engineering/content/contextual-prompting@5TNK1KcSzh9GTKiEJnM-y.md

@@ -5,4 +5,4 @@ Contextual prompting provides specific background information or situational det
 Visit the following resources to learn more:
 
 - [@official@Prompting Best Practices - Anthropic](https://platform.claude.com/docs/en/build-with-claude/prompt-engineering/claude-prompting-best-practices)
-- [@article@Prompt Structure and Key Parts - LearnPrompting](https://learnprompting.org/docs/basics/prompt_structure)
+- [@article@Prompt Structure and Key Parts - LearnPrompting](https://learnprompting.org/docs/basics/prompt_structure)

src/data/roadmaps/prompt-engineering/content/fine-tuning-vs-prompt-engg@Ke5GT163k_ek9SzbcbBGE.md

@@ -5,4 +5,4 @@ Fine-tuning trains models on specific data to specialize behavior, while prompt
 Visit the following resources to learn more:
 
 - [@article@When to use prompt engineering vs. fine-tuning - TechTarget](https://www.techtarget.com/searchEnterpriseAI/tip/Prompt-engineering-vs-fine-tuning-Whats-the-difference)
-- [@article@Prompt Engineering vs Fine Tuning: When to Use Each - Codecademy](https://www.codecademy.com/article/prompt-engineering-vs-fine-tuning)
+- [@article@Prompt Engineering vs Fine Tuning: When to Use Each - Codecademy](https://www.codecademy.com/article/prompt-engineering-vs-fine-tuning)

src/data/roadmaps/prompt-engineering/content/frequency-penalty@YIVNjkmTOY61VmL0md9Pj.md

@@ -4,4 +4,4 @@ Frequency penalty reduces token probability based on how frequently they have ap
 
 Visit the following resources to learn more:
 
-- [@article@Frequency Penalty - LLM Parameter Guide - Vellum](https://www.vellum.ai/llm-parameters/frequency-penalty)
+- [@article@Frequency Penalty - LLM Parameter Guide - Vellum](https://www.vellum.ai/llm-parameters/frequency-penalty)

src/data/roadmaps/prompt-engineering/content/google@o-6UKLZ6oCRbAKgRjH2uI.md

@@ -5,4 +5,4 @@ Google develops Gemini, a family of multimodal AI models. The latest flagship, G
 Visit the following resources to learn more:
 
 - [@official@Google AI Studio](https://ai.google.dev/)
-- [@official@Gemini API Documentation](https://ai.google.dev/gemini-api/docs)
+- [@official@Gemini API Documentation](https://ai.google.dev/gemini-api/docs)

src/data/roadmaps/prompt-engineering/content/hallucination@SWDa3Su3VS815WQbvvNsa.md

@@ -5,4 +5,4 @@ Hallucination in LLMs refers to generating plausible-sounding but factually inco
 Visit the following resources to learn more:
 
 - [@official@Reduce hallucinations - Anthropic](https://platform.claude.com/docs/en/test-and-evaluate/strengthen-guardrails/reduce-hallucinations)
-- [@article@What are AI hallucinations? - IBM](https://www.ibm.com/think/topics/ai-hallucinations)
+- [@article@What are AI hallucinations? - IBM](https://www.ibm.com/think/topics/ai-hallucinations)

src/data/roadmaps/prompt-engineering/content/introduction@jrH1qE6EnFXL4fTyYU8gR.md

@@ -4,4 +4,4 @@ Prompt engineering is the practice of designing effective inputs for Large Langu
 
 Visit the following resources to learn more:
 
-- [@article@What is Generative AI? - LearnPrompting](https://learnprompting.org/docs/basics/generative_ai)
+- [@article@What is Generative AI? - LearnPrompting](https://learnprompting.org/docs/basics/generative_ai)

src/data/roadmaps/prompt-engineering/content/llm-self-evaluation@CvV3GIvQhsTvE-TQjTpIQ.md

@@ -4,4 +4,4 @@ LLM self-evaluation involves prompting models to assess their own outputs for qu
 
 Visit the following resources to learn more:
 
-- [@article@LLM Self-Evaluation - LearnPrompting](https://learnprompting.org/docs/reliability/lm_self_eval)
+- [@article@LLM Self-Evaluation - LearnPrompting](https://learnprompting.org/docs/reliability/lm_self_eval)

src/data/roadmaps/prompt-engineering/content/llm@pamV5Z8DRKk2ioZbg6QVK.md

@@ -5,4 +5,4 @@ Large Language Models (LLMs) are AI systems trained on vast text data to underst
 Visit the following resources to learn more:
 
 - [@official@LLM - Anthropic Glossary](https://platform.claude.com/docs/en/about-claude/glossary)
-- [@article@Differences Between Chatbots and LLMs - LearnPrompting](https://learnprompting.org/docs/basics/chatbot_basics)
+- [@article@Differences Between Chatbots and LLMs - LearnPrompting](https://learnprompting.org/docs/basics/chatbot_basics)

src/data/roadmaps/prompt-engineering/content/llms-and-how-they-work@74JxgfJ_1qmVNZ_QRp9Ne.md

@@ -7,4 +7,4 @@ Visit the following resources to learn more:
 - [@article@What are large language models (LLMs)? - IBM](https://www.ibm.com/think/topics/large-language-models)
 - [@article@Large language model - Wikipedia](https://en.wikipedia.org/wiki/Large_language_model)
 - [@article@How Large Language Models Work: Explained Simply](https://justainews.com/applications/chatbots-and-virtual-assistants/how-large-language-models-work/)
-- [@video@How Large Language Models Work](https://youtu.be/5sLYAQS9sWQ)
+- [@video@How Large Language Models Work](https://youtu.be/5sLYAQS9sWQ)

src/data/roadmaps/prompt-engineering/content/max-tokens@vK9Gf8dGu2UvvJJhhuHG9.md

@@ -5,4 +5,4 @@ Max tokens setting controls the maximum number of tokens an LLM can generate in
 Visit the following resources to learn more:
 
 - [@official@Token Counting - Anthropic](https://platform.claude.com/docs/en/build-with-claude/token-counting)
-- [@article@Max Tokens - LLM Parameter Guide - Vellum](https://www.vellum.ai/llm-parameters/max-tokens)
+- [@article@Max Tokens - LLM Parameter Guide - Vellum](https://www.vellum.ai/llm-parameters/max-tokens)

src/data/roadmaps/prompt-engineering/content/meta@Td2YzDFT4LPGDw8JMmQSQ.md

@@ -5,4 +5,4 @@ Meta develops the Llama family of open-source large language models. The latest
 Visit the following resources to learn more:
 
 - [@official@Llama](https://www.llama.com/)
-- [@opensource@Llama Models (GitHub)](https://github.com/meta-llama/llama-models)
+- [@opensource@Llama Models (GitHub)](https://github.com/meta-llama/llama-models)

src/data/roadmaps/prompt-engineering/content/model-weights--parameters@yfsjW1eze8mWT0iHxv078.md

@@ -4,4 +4,4 @@ Model weights and parameters are the learned values that define an LLM's behavio
 
 Visit the following resources to learn more:
 
-- [@article@What are LLM parameters? - IBM](https://www.ibm.com/think/topics/llm-parameters)
+- [@article@What are LLM parameters? - IBM](https://www.ibm.com/think/topics/llm-parameters)

src/data/roadmaps/prompt-engineering/content/one-shot--few-shot-prompting@Iufv_LsgUNls-Alx_Btlh.md

@@ -7,4 +7,4 @@ Visit the following resources to learn more:
 - [@article@Few-Shot Prompting - DAIR.AI](https://www.promptingguide.ai/techniques/fewshot)
 - [@article@Few-Shot Prompting - LearnPrompting](https://learnprompting.org/docs/basics/few_shot)
 - [@article@Few-Shot Introduction - LearnPrompting](https://learnprompting.org/docs/advanced/few_shot/introduction)
-- [@video@Context Engineering vs. Prompt Engineering: Smarter AI with RAG & Agents](https://youtu.be/vD0E3EUb8-8?si=Fi2igdPTBUocqnX7&t=177)
+- [@video@Context Engineering vs. Prompt Engineering: Smarter AI with RAG & Agents](https://youtu.be/vD0E3EUb8-8?si=Fi2igdPTBUocqnX7&t=177)

src/data/roadmaps/prompt-engineering/content/openai@Yb5cQiV2ETxPbBYCLOpt2.md

@@ -5,4 +5,4 @@ OpenAI develops leading language models including GPT-5.4, o3, and Codex, settin
 Visit the following resources to learn more:
 
 - [@official@OpenAI API Documentation](https://developers.openai.com/api/docs)
-- [@official@OpenAI Cookbook (GitHub)](https://github.com/openai/openai-cookbook)
+- [@official@OpenAI Cookbook (GitHub)](https://github.com/openai/openai-cookbook)

src/data/roadmaps/prompt-engineering/content/output-control@wSf7Zr8ZYBuKWX0GQX6J3.md

@@ -5,4 +5,4 @@ Output control encompasses techniques and parameters for managing LLM response c
 Visit the following resources to learn more:
 
 - [@official@Increase Output Consistency - Anthropic](https://platform.claude.com/docs/en/test-and-evaluate/strengthen-guardrails/increase-consistency)
-- [@article@General Tips for Designing Prompts - DAIR.AI](https://www.promptingguide.ai/introduction/tips)
+- [@article@General Tips for Designing Prompts - DAIR.AI](https://www.promptingguide.ai/introduction/tips)

src/data/roadmaps/prompt-engineering/content/presence-penalty@WpO8V5caudySVehOcuDvK.md

@@ -4,4 +4,4 @@ Presence penalty reduces the likelihood of repeating tokens that have already ap
 
 Visit the following resources to learn more:
 
-- [@article@Presence Penalty - LLM Parameter Guide - Vellum](https://www.vellum.ai/llm-parameters/presence-penalty)
+- [@article@Presence Penalty - LLM Parameter Guide - Vellum](https://www.vellum.ai/llm-parameters/presence-penalty)

src/data/roadmaps/prompt-engineering/content/prompt-debiasing@0H2keZYD8iTNyBgmNVhto.md

@@ -4,4 +4,4 @@ Prompt debiasing involves techniques to reduce unwanted biases in LLM outputs by
 
 Visit the following resources to learn more:
 
-- [@article@Prompt Debiasing - LearnPrompting](https://learnprompting.org/docs/reliability/debiasing)
+- [@article@Prompt Debiasing - LearnPrompting](https://learnprompting.org/docs/reliability/debiasing)

src/data/roadmaps/prompt-engineering/content/prompt-ensembling@HOqWHqAkxLX8f2ImSmZE7.md

@@ -4,4 +4,4 @@ Prompt ensembling combines multiple different prompts or prompt variations to im
 
 Visit the following resources to learn more:
 
-- [@article@Introduction to Ensembling - LearnPrompting](https://learnprompting.org/docs/advanced/ensembling/introduction)
+- [@article@Introduction to Ensembling - LearnPrompting](https://learnprompting.org/docs/advanced/ensembling/introduction)

src/data/roadmaps/prompt-engineering/content/prompt-injection@6W_ONYREbXHwPigoDx1cW.md

@@ -6,4 +6,4 @@ Visit the following resources to learn more:
 
 - [@official@Mitigate jailbreaks and prompt injections - Anthropic](https://platform.claude.com/docs/en/test-and-evaluate/strengthen-guardrails/mitigate-jailbreaks)
 - [@official@LLM01:2025 Prompt Injection - OWASP](https://genai.owasp.org/llmrisk/llm01-prompt-injection/)
-- [@video@What Is a Prompt Injection Attack?](https://www.youtube.com/watch?v=jrHRe9lSqqA)
+- [@video@What Is a Prompt Injection Attack?](https://www.youtube.com/watch?v=jrHRe9lSqqA)

src/data/roadmaps/prompt-engineering/content/rag@gxydtFKmnXNY9I5kpTwjP.md

@@ -4,5 +4,5 @@ Retrieval-Augmented Generation (RAG) combines LLMs with external knowledge retri
 
 Visit the following resources to learn more:
 
-- [@article@Retrieval Augmented Generation (RAG) - DAIR.AI](https://www.promptingguide.ai/techniques/rag)
 - [@opensource@Introduction to RAG - LlamaIndex](https://developers.llamaindex.ai/python/framework/understanding/rag/)
+- [@article@Retrieval Augmented Generation (RAG) - DAIR.AI](https://www.promptingguide.ai/techniques/rag)

src/data/roadmaps/prompt-engineering/content/react-prompting@8Ks6txRSUfMK7VotSQ4sC.md

@@ -6,4 +6,4 @@ Visit the following resources to learn more:
 
 - [@article@ReAct - DAIR.AI](https://www.promptingguide.ai/techniques/react)
 - [@article@ReAct: Synergizing Reasoning and Acting - LearnPrompting](https://learnprompting.org/docs/techniques/react)
-- [@video@4 Methods of Prompt Engineering](https://youtu.be/vD0E3EUb8-8?si=Y6MCLPzjmhMB4jSu&t=203)
+- [@video@4 Methods of Prompt Engineering](https://youtu.be/vD0E3EUb8-8?si=Y6MCLPzjmhMB4jSu&t=203)

src/data/roadmaps/prompt-engineering/content/repetition-penalties@g8ylIg4Zh567u-E3yVVY4.md

@@ -4,4 +4,4 @@ Repetition penalties discourage LLMs from repeating words or phrases by reducing
 
 Visit the following resources to learn more:
 
-- [@article@Tips for Writing Better Prompts - LearnPrompting](https://learnprompting.org/docs/basics/ai_prompt_tips)
+- [@article@Tips for Writing Better Prompts - LearnPrompting](https://learnprompting.org/docs/basics/ai_prompt_tips)

src/data/roadmaps/prompt-engineering/content/role-prompting@XHWKGaSRBYT4MsCHwV-iR.md

@@ -6,4 +6,4 @@ Visit the following resources to learn more:
 
 - [@article@Assigning Roles to Chatbots - LearnPrompting](https://learnprompting.org/docs/basics/roles)
 - [@article@Role Prompting - LearnPrompting](https://learnprompting.org/docs/advanced/zero_shot/role_prompting)
-- [@video@Context Engineering vs. Prompt Engineering: Smarter AI with RAG & Agents](https://youtu.be/vD0E3EUb8-8?si=9orzEniOGmRD7g-o&t=136)
+- [@video@Context Engineering vs. Prompt Engineering: Smarter AI with RAG & Agents](https://youtu.be/vD0E3EUb8-8?si=9orzEniOGmRD7g-o&t=136)

src/data/roadmaps/prompt-engineering/content/sampling-parameters@JgigM7HvmNOuKnp60v1Ce.md

@@ -4,4 +4,4 @@ Sampling parameters (temperature, top-K, top-P) control how LLMs select tokens f
 
 Visit the following resources to learn more:
 
-- [@article@LLM Settings (Temperature, Top-K, Top-P) - DAIR.AI](https://www.promptingguide.ai/introduction/settings)
+- [@article@LLM Settings (Temperature, Top-K, Top-P) - DAIR.AI](https://www.promptingguide.ai/introduction/settings)

src/data/roadmaps/prompt-engineering/content/self-consistency-prompting@1EzqCoplXPiHjp9Z-vqn-.md

@@ -5,4 +5,4 @@ Self-consistency prompting generates multiple reasoning paths for the same probl
 Visit the following resources to learn more:
 
 - [@article@Self-Consistency - DAIR.AI](https://www.promptingguide.ai/techniques/consistency)
-- [@article@Self-Consistency - LearnPrompting](https://learnprompting.org/docs/intermediate/self_consistency)
+- [@article@Self-Consistency - LearnPrompting](https://learnprompting.org/docs/intermediate/self_consistency)

src/data/roadmaps/prompt-engineering/content/step-back-prompting@2MboHh8ugkoH8dSd9d4Mk.md

@@ -4,4 +4,4 @@ Step-back prompting improves LLM performance by first asking a general question
 
 Visit the following resources to learn more:
 
-- [@article@Step-Back Prompting - LearnPrompting](https://learnprompting.org/docs/advanced/thought_generation/step_back_prompting)
+- [@article@Step-Back Prompting - LearnPrompting](https://learnprompting.org/docs/advanced/thought_generation/step_back_prompting)

src/data/roadmaps/prompt-engineering/content/stop-sequences@v3CylRlojeltcwnE76j8Q.md

@@ -5,4 +5,4 @@ Stop sequences are specific strings that signal the LLM to stop generating text
 Visit the following resources to learn more:
 
 - [@official@Handling Stop Reasons - Anthropic](https://platform.claude.com/docs/en/build-with-claude/handling-stop-reasons)
-- [@article@Stop Sequence - LLM Parameter Guide - Vellum](https://www.vellum.ai/llm-parameters/stop-sequence)
+- [@article@Stop Sequence - LLM Parameter Guide - Vellum](https://www.vellum.ai/llm-parameters/stop-sequence)

src/data/roadmaps/prompt-engineering/content/structured-outputs@j-PWO-ZmF9Oi9A5bwMRto.md

@@ -7,4 +7,4 @@ Visit the following resources to learn more:
 - [@official@Structured Output - Google Gemini API](https://ai.google.dev/gemini-api/docs/structured-output)
 - [@official@Structured Outputs - Anthropic](https://platform.claude.com/docs/en/build-with-claude/structured-outputs)
 - [@opensource@Instructor - Structured Output Library](https://github.com/jxnl/instructor)
-- [@article@Structured Outputs - LLM Parameter Guide - Vellum](https://www.vellum.ai/llm-parameters/structured-outputs)
+- [@article@Structured Outputs - LLM Parameter Guide - Vellum](https://www.vellum.ai/llm-parameters/structured-outputs)

src/data/roadmaps/prompt-engineering/content/system-prompting@fWo39-hehRgwmx7CF36mM.md

@@ -5,4 +5,4 @@ System prompting sets the overall context, purpose, and operational guidelines f
 Visit the following resources to learn more:
 
 - [@official@Prompt Engineering Overview - Anthropic](https://platform.claude.com/docs/en/build-with-claude/prompt-engineering/overview)
-- [@article@Instructions - LearnPrompting](https://learnprompting.org/docs/basics/instructions)
+- [@article@Instructions - LearnPrompting](https://learnprompting.org/docs/basics/instructions)

src/data/roadmaps/prompt-engineering/content/temperature@iMwg-I76-Tg5dhu8DGO6U.md

@@ -5,4 +5,4 @@ Temperature controls the randomness in token selection during text generation. L
 Visit the following resources to learn more:
 
 - [@article@What is LLM Temperature? - IBM](https://www.ibm.com/think/topics/llm-temperature)
-- [@article@Temperature - LLM Parameter Guide - Vellum](https://www.vellum.ai/llm-parameters/temperature)
+- [@article@Temperature - LLM Parameter Guide - Vellum](https://www.vellum.ai/llm-parameters/temperature)

src/data/roadmaps/prompt-engineering/content/tokens@NPcaSEteeEA5g22wQ7nL_.md

@@ -5,4 +5,4 @@ Tokens are fundamental units of text that LLMs process, created by breaking down
 Visit the following resources to learn more:
 
 - [@article@Understanding tokens - Microsoft Learn](https://learn.microsoft.com/en-us/dotnet/ai/conceptual/understanding-tokens)
-- [@article@What Are Tokens in LLMs and Why They Matter - LLM Guides](https://llmguides.ai/learn/what-are-tokens/)
+- [@article@What Are Tokens in LLMs and Why They Matter - LLM Guides](https://llmguides.ai/learn/what-are-tokens/)

src/data/roadmaps/prompt-engineering/content/top-k@FF8ai1v5GDzxXLQhpwuPj.md

@@ -5,4 +5,4 @@ Top-K restricts token selection to the K most likely tokens from the probability
 Visit the following resources to learn more:
 
 - [@official@Gemini API Prompting Strategies - Google](https://ai.google.dev/gemini-api/docs/prompting-strategies)
-- [@article@Top K - LLM Parameter Guide - Vellum](https://www.vellum.ai/llm-parameters/top-k)
+- [@article@Top K - LLM Parameter Guide - Vellum](https://www.vellum.ai/llm-parameters/top-k)

src/data/roadmaps/prompt-engineering/content/top-p@-G1U1jDN5st1fTUtQmMl1.md

@@ -4,4 +4,4 @@ Top-P (nucleus sampling) selects tokens from the smallest set whose cumulative p
 
 Visit the following resources to learn more:
 
-- [@article@Top P - LLM Parameter Guide - Vellum](https://www.vellum.ai/llm-parameters/top-p)
+- [@article@Top P - LLM Parameter Guide - Vellum](https://www.vellum.ai/llm-parameters/top-p)

src/data/roadmaps/prompt-engineering/content/tree-of-thoughts-tot-prompting@ob9D0W9B9145Da64nbi1M.md

@@ -4,4 +4,4 @@ Tree of Thoughts (ToT) generalizes Chain of Thought by allowing LLMs to explore
 
 Visit the following resources to learn more:
 
-- [@article@Tree of Thoughts - DAIR.AI](https://www.promptingguide.ai/techniques/tot)
+- [@article@Tree of Thoughts - DAIR.AI](https://www.promptingguide.ai/techniques/tot)