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@@ -5,4 +5,4 @@ AI agents are autonomous systems that use LLMs to reason, plan, and take actions
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Visit the following resources to learn more:
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- [@official@Tool use overview - Anthropic](https://platform.claude.com/docs/en/agents-and-tools/tool-use/overview)
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- [@article@Introduction to AI Agents - DAIR.AI](https://www.promptingguide.ai/agents/introduction)
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- [@article@Introduction to AI Agents - DAIR.AI](https://www.promptingguide.ai/agents/introduction)
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+1
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@@ -4,6 +4,4 @@ AI red teaming involves deliberately testing AI systems to find vulnerabilities,
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Visit the following resources to learn more:
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- [@official@Define success and build evaluations - Anthropic](https://platform.claude.com/docs/en/test-and-evaluate/develop-tests)
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- [@official@OWASP Top 10 for LLM Applications 2025](https://genai.owasp.org/llmrisk/)
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- [@opensource@Microsoft PyRIT - Risk Identification for GenAI](https://github.com/microsoft/PyRIT)
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- [@roadmap@Visit the Dedicated AI Red Teaming Roadmap](https://roadmap.sh/ai-red-teaming)
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@@ -4,4 +4,4 @@ AI (Artificial Intelligence) refers to systems that perform specific tasks intel
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Visit the following resources to learn more:
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- [@article@Artificial general intelligence - Wikipedia](https://en.wikipedia.org/wiki/Artificial_general_intelligence)
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- [@article@Artificial general intelligence - Wikipedia](https://en.wikipedia.org/wiki/Artificial_general_intelligence)
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@@ -5,4 +5,4 @@ Anthropic develops Claude, a family of large language models focused on safety a
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Visit the following resources to learn more:
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- [@official@Claude API Documentation](https://docs.anthropic.com/en/docs/intro)
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- [@official@Anthropic Research](https://www.anthropic.com/research)
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- [@official@Anthropic Research](https://www.anthropic.com/research)
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+1
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@@ -4,4 +4,4 @@ Automatic Prompt Engineering (APE) uses LLMs to generate and optimize prompts au
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Visit the following resources to learn more:
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- [@article@Automatic Prompt Engineer - DAIR.AI](https://www.promptingguide.ai/techniques/ape)
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- [@article@Automatic Prompt Engineer - DAIR.AI](https://www.promptingguide.ai/techniques/ape)
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+1
-1
@@ -4,4 +4,4 @@ Calibrating LLMs involves adjusting models so their confidence scores accurately
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Visit the following resources to learn more:
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- [@article@Calibrating LLMs - LearnPrompting](https://learnprompting.org/docs/reliability/calibration)
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- [@article@Calibrating LLMs - LearnPrompting](https://learnprompting.org/docs/reliability/calibration)
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+1
-1
@@ -7,4 +7,4 @@ Visit the following resources to learn more:
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- [@article@Chain-of-Thought Prompting - DAIR.AI](https://www.promptingguide.ai/techniques/cot)
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- [@article@Chain-of-Thought Prompting - LearnPrompting](https://learnprompting.org/docs/intermediate/chain_of_thought)
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- [@article@Reasoning LLMs Guide - DAIR.AI](https://www.promptingguide.ai/guides/reasoning-llms)
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- [@video@Context Engineering vs. Prompt Engineering: Smarter AI with RAG & Agents](https://youtu.be/vD0E3EUb8-8?si=Y6MCLPzjmhMB4jSu&t=203)
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- [@video@Context Engineering vs. Prompt Engineering: Smarter AI with RAG & Agents](https://youtu.be/vD0E3EUb8-8?si=Y6MCLPzjmhMB4jSu&t=203)
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+1
-1
@@ -5,4 +5,4 @@ Context window refers to the maximum number of tokens an LLM can process in a si
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Visit the following resources to learn more:
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- [@official@Context windows - Anthropic](https://platform.claude.com/docs/en/build-with-claude/context-windows)
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- [@article@What is a context window? - IBM](https://www.ibm.com/think/topics/context-window)
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- [@article@What is a context window? - IBM](https://www.ibm.com/think/topics/context-window)
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+1
-1
@@ -5,4 +5,4 @@ Contextual prompting provides specific background information or situational det
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Visit the following resources to learn more:
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- [@official@Prompting Best Practices - Anthropic](https://platform.claude.com/docs/en/build-with-claude/prompt-engineering/claude-prompting-best-practices)
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- [@article@Prompt Structure and Key Parts - LearnPrompting](https://learnprompting.org/docs/basics/prompt_structure)
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- [@article@Prompt Structure and Key Parts - LearnPrompting](https://learnprompting.org/docs/basics/prompt_structure)
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+1
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@@ -5,4 +5,4 @@ Fine-tuning trains models on specific data to specialize behavior, while prompt
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Visit the following resources to learn more:
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- [@article@When to use prompt engineering vs. fine-tuning - TechTarget](https://www.techtarget.com/searchEnterpriseAI/tip/Prompt-engineering-vs-fine-tuning-Whats-the-difference)
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- [@article@Prompt Engineering vs Fine Tuning: When to Use Each - Codecademy](https://www.codecademy.com/article/prompt-engineering-vs-fine-tuning)
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- [@article@Prompt Engineering vs Fine Tuning: When to Use Each - Codecademy](https://www.codecademy.com/article/prompt-engineering-vs-fine-tuning)
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+1
-1
@@ -4,4 +4,4 @@ Frequency penalty reduces token probability based on how frequently they have ap
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Visit the following resources to learn more:
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- [@article@Frequency Penalty - LLM Parameter Guide - Vellum](https://www.vellum.ai/llm-parameters/frequency-penalty)
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- [@article@Frequency Penalty - LLM Parameter Guide - Vellum](https://www.vellum.ai/llm-parameters/frequency-penalty)
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@@ -5,4 +5,4 @@ Google develops Gemini, a family of multimodal AI models. The latest flagship, G
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Visit the following resources to learn more:
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- [@official@Google AI Studio](https://ai.google.dev/)
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- [@official@Gemini API Documentation](https://ai.google.dev/gemini-api/docs)
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- [@official@Gemini API Documentation](https://ai.google.dev/gemini-api/docs)
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@@ -5,4 +5,4 @@ Hallucination in LLMs refers to generating plausible-sounding but factually inco
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Visit the following resources to learn more:
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- [@official@Reduce hallucinations - Anthropic](https://platform.claude.com/docs/en/test-and-evaluate/strengthen-guardrails/reduce-hallucinations)
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- [@article@What are AI hallucinations? - IBM](https://www.ibm.com/think/topics/ai-hallucinations)
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- [@article@What are AI hallucinations? - IBM](https://www.ibm.com/think/topics/ai-hallucinations)
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@@ -4,4 +4,4 @@ Prompt engineering is the practice of designing effective inputs for Large Langu
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Visit the following resources to learn more:
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- [@article@What is Generative AI? - LearnPrompting](https://learnprompting.org/docs/basics/generative_ai)
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- [@article@What is Generative AI? - LearnPrompting](https://learnprompting.org/docs/basics/generative_ai)
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+1
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@@ -4,4 +4,4 @@ LLM self-evaluation involves prompting models to assess their own outputs for qu
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Visit the following resources to learn more:
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- [@article@LLM Self-Evaluation - LearnPrompting](https://learnprompting.org/docs/reliability/lm_self_eval)
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- [@article@LLM Self-Evaluation - LearnPrompting](https://learnprompting.org/docs/reliability/lm_self_eval)
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@@ -5,4 +5,4 @@ Large Language Models (LLMs) are AI systems trained on vast text data to underst
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Visit the following resources to learn more:
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- [@official@LLM - Anthropic Glossary](https://platform.claude.com/docs/en/about-claude/glossary)
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- [@article@Differences Between Chatbots and LLMs - LearnPrompting](https://learnprompting.org/docs/basics/chatbot_basics)
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- [@article@Differences Between Chatbots and LLMs - LearnPrompting](https://learnprompting.org/docs/basics/chatbot_basics)
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+1
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@@ -7,4 +7,4 @@ Visit the following resources to learn more:
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- [@article@What are large language models (LLMs)? - IBM](https://www.ibm.com/think/topics/large-language-models)
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- [@article@Large language model - Wikipedia](https://en.wikipedia.org/wiki/Large_language_model)
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- [@article@How Large Language Models Work: Explained Simply](https://justainews.com/applications/chatbots-and-virtual-assistants/how-large-language-models-work/)
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- [@video@How Large Language Models Work](https://youtu.be/5sLYAQS9sWQ)
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- [@video@How Large Language Models Work](https://youtu.be/5sLYAQS9sWQ)
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@@ -5,4 +5,4 @@ Max tokens setting controls the maximum number of tokens an LLM can generate in
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Visit the following resources to learn more:
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- [@official@Token Counting - Anthropic](https://platform.claude.com/docs/en/build-with-claude/token-counting)
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- [@article@Max Tokens - LLM Parameter Guide - Vellum](https://www.vellum.ai/llm-parameters/max-tokens)
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- [@article@Max Tokens - LLM Parameter Guide - Vellum](https://www.vellum.ai/llm-parameters/max-tokens)
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@@ -5,4 +5,4 @@ Meta develops the Llama family of open-source large language models. The latest
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Visit the following resources to learn more:
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- [@official@Llama](https://www.llama.com/)
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- [@opensource@Llama Models (GitHub)](https://github.com/meta-llama/llama-models)
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- [@opensource@Llama Models (GitHub)](https://github.com/meta-llama/llama-models)
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+1
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@@ -4,4 +4,4 @@ Model weights and parameters are the learned values that define an LLM's behavio
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Visit the following resources to learn more:
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- [@article@What are LLM parameters? - IBM](https://www.ibm.com/think/topics/llm-parameters)
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- [@article@What are LLM parameters? - IBM](https://www.ibm.com/think/topics/llm-parameters)
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+1
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@@ -7,4 +7,4 @@ Visit the following resources to learn more:
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- [@article@Few-Shot Prompting - DAIR.AI](https://www.promptingguide.ai/techniques/fewshot)
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- [@article@Few-Shot Prompting - LearnPrompting](https://learnprompting.org/docs/basics/few_shot)
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- [@article@Few-Shot Introduction - LearnPrompting](https://learnprompting.org/docs/advanced/few_shot/introduction)
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- [@video@Context Engineering vs. Prompt Engineering: Smarter AI with RAG & Agents](https://youtu.be/vD0E3EUb8-8?si=Fi2igdPTBUocqnX7&t=177)
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- [@video@Context Engineering vs. Prompt Engineering: Smarter AI with RAG & Agents](https://youtu.be/vD0E3EUb8-8?si=Fi2igdPTBUocqnX7&t=177)
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@@ -5,4 +5,4 @@ OpenAI develops leading language models including GPT-5.4, o3, and Codex, settin
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Visit the following resources to learn more:
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- [@official@OpenAI API Documentation](https://developers.openai.com/api/docs)
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- [@official@OpenAI Cookbook (GitHub)](https://github.com/openai/openai-cookbook)
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- [@official@OpenAI Cookbook (GitHub)](https://github.com/openai/openai-cookbook)
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+1
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@@ -5,4 +5,4 @@ Output control encompasses techniques and parameters for managing LLM response c
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Visit the following resources to learn more:
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- [@official@Increase Output Consistency - Anthropic](https://platform.claude.com/docs/en/test-and-evaluate/strengthen-guardrails/increase-consistency)
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- [@article@General Tips for Designing Prompts - DAIR.AI](https://www.promptingguide.ai/introduction/tips)
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- [@article@General Tips for Designing Prompts - DAIR.AI](https://www.promptingguide.ai/introduction/tips)
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@@ -4,4 +4,4 @@ Presence penalty reduces the likelihood of repeating tokens that have already ap
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Visit the following resources to learn more:
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- [@article@Presence Penalty - LLM Parameter Guide - Vellum](https://www.vellum.ai/llm-parameters/presence-penalty)
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- [@article@Presence Penalty - LLM Parameter Guide - Vellum](https://www.vellum.ai/llm-parameters/presence-penalty)
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@@ -4,4 +4,4 @@ Prompt debiasing involves techniques to reduce unwanted biases in LLM outputs by
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Visit the following resources to learn more:
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- [@article@Prompt Debiasing - LearnPrompting](https://learnprompting.org/docs/reliability/debiasing)
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- [@article@Prompt Debiasing - LearnPrompting](https://learnprompting.org/docs/reliability/debiasing)
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@@ -4,4 +4,4 @@ Prompt ensembling combines multiple different prompts or prompt variations to im
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Visit the following resources to learn more:
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- [@article@Introduction to Ensembling - LearnPrompting](https://learnprompting.org/docs/advanced/ensembling/introduction)
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- [@article@Introduction to Ensembling - LearnPrompting](https://learnprompting.org/docs/advanced/ensembling/introduction)
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@@ -6,4 +6,4 @@ Visit the following resources to learn more:
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- [@official@Mitigate jailbreaks and prompt injections - Anthropic](https://platform.claude.com/docs/en/test-and-evaluate/strengthen-guardrails/mitigate-jailbreaks)
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- [@official@LLM01:2025 Prompt Injection - OWASP](https://genai.owasp.org/llmrisk/llm01-prompt-injection/)
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- [@video@What Is a Prompt Injection Attack?](https://www.youtube.com/watch?v=jrHRe9lSqqA)
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- [@video@What Is a Prompt Injection Attack?](https://www.youtube.com/watch?v=jrHRe9lSqqA)
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@@ -4,5 +4,5 @@ Retrieval-Augmented Generation (RAG) combines LLMs with external knowledge retri
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Visit the following resources to learn more:
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- [@article@Retrieval Augmented Generation (RAG) - DAIR.AI](https://www.promptingguide.ai/techniques/rag)
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- [@opensource@Introduction to RAG - LlamaIndex](https://developers.llamaindex.ai/python/framework/understanding/rag/)
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- [@article@Retrieval Augmented Generation (RAG) - DAIR.AI](https://www.promptingguide.ai/techniques/rag)
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@@ -6,4 +6,4 @@ Visit the following resources to learn more:
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- [@article@ReAct - DAIR.AI](https://www.promptingguide.ai/techniques/react)
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- [@article@ReAct: Synergizing Reasoning and Acting - LearnPrompting](https://learnprompting.org/docs/techniques/react)
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- [@video@4 Methods of Prompt Engineering](https://youtu.be/vD0E3EUb8-8?si=Y6MCLPzjmhMB4jSu&t=203)
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- [@video@4 Methods of Prompt Engineering](https://youtu.be/vD0E3EUb8-8?si=Y6MCLPzjmhMB4jSu&t=203)
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@@ -4,4 +4,4 @@ Repetition penalties discourage LLMs from repeating words or phrases by reducing
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Visit the following resources to learn more:
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- [@article@Tips for Writing Better Prompts - LearnPrompting](https://learnprompting.org/docs/basics/ai_prompt_tips)
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- [@article@Tips for Writing Better Prompts - LearnPrompting](https://learnprompting.org/docs/basics/ai_prompt_tips)
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@@ -6,4 +6,4 @@ Visit the following resources to learn more:
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- [@article@Assigning Roles to Chatbots - LearnPrompting](https://learnprompting.org/docs/basics/roles)
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- [@article@Role Prompting - LearnPrompting](https://learnprompting.org/docs/advanced/zero_shot/role_prompting)
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- [@video@Context Engineering vs. Prompt Engineering: Smarter AI with RAG & Agents](https://youtu.be/vD0E3EUb8-8?si=9orzEniOGmRD7g-o&t=136)
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- [@video@Context Engineering vs. Prompt Engineering: Smarter AI with RAG & Agents](https://youtu.be/vD0E3EUb8-8?si=9orzEniOGmRD7g-o&t=136)
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@@ -4,4 +4,4 @@ Sampling parameters (temperature, top-K, top-P) control how LLMs select tokens f
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Visit the following resources to learn more:
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- [@article@LLM Settings (Temperature, Top-K, Top-P) - DAIR.AI](https://www.promptingguide.ai/introduction/settings)
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- [@article@LLM Settings (Temperature, Top-K, Top-P) - DAIR.AI](https://www.promptingguide.ai/introduction/settings)
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@@ -5,4 +5,4 @@ Self-consistency prompting generates multiple reasoning paths for the same probl
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Visit the following resources to learn more:
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- [@article@Self-Consistency - DAIR.AI](https://www.promptingguide.ai/techniques/consistency)
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- [@article@Self-Consistency - LearnPrompting](https://learnprompting.org/docs/intermediate/self_consistency)
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- [@article@Self-Consistency - LearnPrompting](https://learnprompting.org/docs/intermediate/self_consistency)
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+1
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@@ -4,4 +4,4 @@ Step-back prompting improves LLM performance by first asking a general question
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Visit the following resources to learn more:
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- [@article@Step-Back Prompting - LearnPrompting](https://learnprompting.org/docs/advanced/thought_generation/step_back_prompting)
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- [@article@Step-Back Prompting - LearnPrompting](https://learnprompting.org/docs/advanced/thought_generation/step_back_prompting)
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@@ -5,4 +5,4 @@ Stop sequences are specific strings that signal the LLM to stop generating text
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Visit the following resources to learn more:
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- [@official@Handling Stop Reasons - Anthropic](https://platform.claude.com/docs/en/build-with-claude/handling-stop-reasons)
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- [@article@Stop Sequence - LLM Parameter Guide - Vellum](https://www.vellum.ai/llm-parameters/stop-sequence)
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- [@article@Stop Sequence - LLM Parameter Guide - Vellum](https://www.vellum.ai/llm-parameters/stop-sequence)
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@@ -7,4 +7,4 @@ Visit the following resources to learn more:
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- [@official@Structured Output - Google Gemini API](https://ai.google.dev/gemini-api/docs/structured-output)
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- [@official@Structured Outputs - Anthropic](https://platform.claude.com/docs/en/build-with-claude/structured-outputs)
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- [@opensource@Instructor - Structured Output Library](https://github.com/jxnl/instructor)
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- [@article@Structured Outputs - LLM Parameter Guide - Vellum](https://www.vellum.ai/llm-parameters/structured-outputs)
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- [@article@Structured Outputs - LLM Parameter Guide - Vellum](https://www.vellum.ai/llm-parameters/structured-outputs)
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@@ -5,4 +5,4 @@ System prompting sets the overall context, purpose, and operational guidelines f
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Visit the following resources to learn more:
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- [@official@Prompt Engineering Overview - Anthropic](https://platform.claude.com/docs/en/build-with-claude/prompt-engineering/overview)
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- [@article@Instructions - LearnPrompting](https://learnprompting.org/docs/basics/instructions)
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- [@article@Instructions - LearnPrompting](https://learnprompting.org/docs/basics/instructions)
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@@ -5,4 +5,4 @@ Temperature controls the randomness in token selection during text generation. L
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Visit the following resources to learn more:
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- [@article@What is LLM Temperature? - IBM](https://www.ibm.com/think/topics/llm-temperature)
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- [@article@Temperature - LLM Parameter Guide - Vellum](https://www.vellum.ai/llm-parameters/temperature)
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- [@article@Temperature - LLM Parameter Guide - Vellum](https://www.vellum.ai/llm-parameters/temperature)
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@@ -5,4 +5,4 @@ Tokens are fundamental units of text that LLMs process, created by breaking down
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Visit the following resources to learn more:
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- [@article@Understanding tokens - Microsoft Learn](https://learn.microsoft.com/en-us/dotnet/ai/conceptual/understanding-tokens)
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- [@article@What Are Tokens in LLMs and Why They Matter - LLM Guides](https://llmguides.ai/learn/what-are-tokens/)
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- [@article@What Are Tokens in LLMs and Why They Matter - LLM Guides](https://llmguides.ai/learn/what-are-tokens/)
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@@ -5,4 +5,4 @@ Top-K restricts token selection to the K most likely tokens from the probability
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Visit the following resources to learn more:
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- [@official@Gemini API Prompting Strategies - Google](https://ai.google.dev/gemini-api/docs/prompting-strategies)
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- [@article@Top K - LLM Parameter Guide - Vellum](https://www.vellum.ai/llm-parameters/top-k)
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- [@article@Top K - LLM Parameter Guide - Vellum](https://www.vellum.ai/llm-parameters/top-k)
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@@ -4,4 +4,4 @@ Top-P (nucleus sampling) selects tokens from the smallest set whose cumulative p
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Visit the following resources to learn more:
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- [@article@Top P - LLM Parameter Guide - Vellum](https://www.vellum.ai/llm-parameters/top-p)
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- [@article@Top P - LLM Parameter Guide - Vellum](https://www.vellum.ai/llm-parameters/top-p)
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@@ -4,4 +4,4 @@ Tree of Thoughts (ToT) generalizes Chain of Thought by allowing LLMs to explore
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Visit the following resources to learn more:
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- [@article@Tree of Thoughts - DAIR.AI](https://www.promptingguide.ai/techniques/tot)
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- [@article@Tree of Thoughts - DAIR.AI](https://www.promptingguide.ai/techniques/tot)
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+1
-1
@@ -5,4 +5,4 @@ A prompt is an input provided to a Large Language Model (LLM) to generate a resp
|
||||
Visit the following resources to learn more:
|
||||
|
||||
- [@article@Basics of Prompting - DAIR.AI](https://www.promptingguide.ai/introduction/basics)
|
||||
- [@article@Prompt Elements - DAIR.AI](https://www.promptingguide.ai/introduction/elements)
|
||||
- [@article@Prompt Elements - DAIR.AI](https://www.promptingguide.ai/introduction/elements)
|
||||
+1
-1
@@ -6,4 +6,4 @@ Visit the following resources to learn more:
|
||||
|
||||
- [@article@Prompt engineering - Wikipedia](https://en.wikipedia.org/wiki/Prompt_engineering)
|
||||
- [@article@Introduction to Prompt Engineering - LearnPrompting](https://learnprompting.org/docs/basics/prompt_engineering)
|
||||
- [@video@RAG vs Fine-Tuning vs Prompt Engineering: Optimizing AI Models](https://youtu.be/zYGDpG-pTho?si=yov4dDrcsHBAkey-&t=522)
|
||||
- [@video@RAG vs Fine-Tuning vs Prompt Engineering: Optimizing AI Models](https://youtu.be/zYGDpG-pTho?si=yov4dDrcsHBAkey-&t=522)
|
||||
@@ -5,4 +5,4 @@ xAI develops Grok, a conversational AI model with real-time web access and integ
|
||||
Visit the following resources to learn more:
|
||||
|
||||
- [@official@xAI Documentation](https://docs.x.ai/)
|
||||
- [@official@xAI API Console](https://console.x.ai)
|
||||
- [@official@xAI API Console](https://console.x.ai)
|
||||
+1
-1
@@ -5,4 +5,4 @@ Zero-shot prompting provides only a task description without examples, relying o
|
||||
Visit the following resources to learn more:
|
||||
|
||||
- [@article@Zero-Shot Prompting - DAIR.AI](https://www.promptingguide.ai/techniques/zeroshot)
|
||||
- [@article@Introduction to Zero-Shot Techniques - LearnPrompting](https://learnprompting.org/docs/advanced/zero_shot/introduction)
|
||||
- [@article@Introduction to Zero-Shot Techniques - LearnPrompting](https://learnprompting.org/docs/advanced/zero_shot/introduction)
|
||||
Reference in New Issue
Block a user