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#llm (35)

Concepts (3)

AI Agents

LLM-driven systems that pursue a goal by interleaving reasoning, tool calls, and observations inside a loop — and that decide for themselves which step to take next.
llm ai tools mcp tool-use autonomy react-pattern multi-agent web-researched

APIs

A versioned contract between two pieces of software — endpoints, verbs, payload shapes, errors, and auth — that decouples a caller from an implementation.
http contract protocol web rest graphql rpc http3 llm attestation web-researched

Retrieval-Augmented Generation (RAG)

Grounding LLM responses in chunks retrieved from an external corpus so the model reasons over real, citable sources instead of parametric memory alone.
llm vector-search ai grounding embeddings hallucination evaluation web-researched

Cheat sheets (32)

ai — Vercel AI SDK

Package-level reference for the Vercel AI SDK — streamText, generateObject, tool calling, structured output, and the multi-provider model interface.
npm package ai llm vercel

openai — official OpenAI Node SDK

Package-level reference for openai on npm — Chat Completions, the Responses API, streaming, tool calls, structured outputs, embeddings, and the v4→v5 migration.
npm package ai llm javascript

autogen-agentchat — Microsoft AutoGen v0.4 Multi-Agent SDK

Package-level reference for the autogen-agentchat / autogen-core / autogen-ext family on PyPI plus the legacy pyautogen — install, rename history, versioning, and alternatives.
pip package llm ai agents multi-agent microsoft

crewai — Role-Based Multi-Agent Orchestration

Package-level reference for the crewai library on PyPI plus the crewai-tools companion — install, versioning, and multi-agent alternatives.
pip package llm ai agents multi-agent

dspy — Declarative LLM Programming

Package-level reference for DSPy on PyPI — the dspy / dspy-ai rename, install variants, version policy, and alternatives.
pip package ai llm

google-genai — Google Gemini SDK (successor to google-generativeai)

Package-level reference for google-genai (the current Gemini SDK) and its predecessor google-generativeai — install, auth, versioning, and alternatives.
pip package llm ai google gemini api

guidance — Programmatic LLM Output Control

Package-level reference for the guidance library on PyPI — install, LLM-provider extras, versioning, and alternatives like instructor and outlines.
pip package llm ai structured-output prompting

langchain — LLM Application Framework Family

Package-level reference for the langchain family on PyPI — install variants, partner packages, version churn, and alternatives.
pip package llm ai langchain agents rag

langsmith — LLM Observability SDK

Package-level reference for the langsmith SDK on PyPI — install, versioning, env-var setup, and observability alternatives.
pip package llm ai observability evaluation tracing

sentence-transformers — Embedding & Reranking Models

Package-level reference for the sentence-transformers library on PyPI — install, transformers/torch deps, model registry, and embedding alternatives.
pip package llm ai embeddings rag huggingface

transformers — Hugging Face Model Library

Package-level reference for the Hugging Face transformers library on PyPI — install extras, backend choice, versioning, and alternatives.
pip package llm ai huggingface nlp ml

Agent Frameworks Comparison — Decision Matrix

Side-by-side comparison of LangChain, LlamaIndex, AutoGen, CrewAI, Haystack, and Semantic Kernel for building LLM-powered applications and agent systems. Covers strengths, weaknesses, and when to pick each.
ai llm agents langchain llamaindex autogen crewai haystack semantic-kernel comparison decision-matrix

DSPy — Programmatic Prompting and Optimisation

Build LLM programs in DSPy with declarative signatures, modules, and optimisers. Covers Predict, ChainOfThought, ReAct, BootstrapFewShot, COPRO, MIPRO, MIPROv2, and inference compilation.
python dspy llm ai prompting optimisation stanford signatures modules

Haystack 2.x — Pipelines for LLM Applications

Build production-grade LLM pipelines with Haystack 2.x. Covers components, the pipeline graph, indexing and querying, retrievers, generators, RAG patterns, and evaluation.
python haystack llm rag ai pipelines deepset retrievers generators

Semantic Kernel — Microsoft's AI Orchestration SDK

Build LLM-powered applications with Microsoft Semantic Kernel. Covers the kernel, plugins, prompt templates, planners, function calling, Kernel Memory, Python and .NET SDKs.
python dotnet csharp semantic-kernel microsoft llm ai plugins planners memory

Prompting

Prompt engineering patterns, RAG, evaluations, few-shot, chain-of-thought, and structured output — foundational techniques for extracting reliable, structured behavior from LLMs.
prompting rag llm prompt-engineering cot few-shot evals structured-output

Chain-of-Thought Prompting

CoT prompting techniques — zero-shot CoT, few-shot CoT, self-consistency, tree of thoughts, and how reasoning models compare with prompted reasoning.
chain-of-thought cot reasoning llm prompting self-consistency tree-of-thoughts thinking

Few-Shot Prompting

In-context learning techniques — example selection, format design, count tuning, dynamic retrieval of demonstrations, and pitfalls of few-shot prompting.
few-shot in-context-learning icl prompting llm examples demonstrations

Prompt Engineering Patterns

Reliable prompt structures for reasoning, extraction, classification, generation, extended thinking, and vision tasks with Claude.
prompting llm ai claude chain-of-thought xml thinking

RAG Implementation Checklist

End-to-end checklist and code for building reliable Retrieval-Augmented Generation pipelines — chunking, embedding, vector DBs, retrieval, and evaluation.
rag ai embeddings vector-db llm retrieval semantic-search

Frameworks

Hugging Face Transformers, LangChain, Google Gemini SDK, and LangSmith — practical reference for AI/ML frameworks and observability tools.
ai llm python ml transformers langchain gemini

AI

Claude Code, Codex CLI, the Claude API, and prompt engineering — practical reference for building with and using large language models.
ai llm claude prompting anthropic codex openai

AutoGen — Multi-Agent Conversations

Build multi-agent AI systems with Microsoft AutoGen. Covers agents, group chats, code execution, tool registration, async runtimes, and LLM configuration.
python autogen agents llm ai microsoft multi-agent code-execution

crewAI — Role-Based Agent Crews

Orchestrate teams of role-playing AI agents with crewAI. Covers agents, tasks, crews, tools, LLM selection, memory, YAML config, and the kickoff lifecycle.
python crewai agents llm ai multi-agent rag tools

google-generativeai — Gemini SDK

Call Google's Gemini models from Python for text, multimodal, streaming, chat, function calling, and embeddings. Covers the genai SDK, safety settings, file API, and async usage.
python google gemini llm ai multimodal api

guidance — Constrained LLM Generation

Interleave Python control flow with LLM generation and enforce structured output using guidance. Covers gen(), select(), chat blocks, regex constraints, JSON schemas, and token healing.
python guidance llm ai structured-output constrained-generation transformers

LangChain — LLM Application Framework

Build LLM-powered pipelines with LangChain. Covers LCEL chains, chat models, prompts, output parsers, tools, agents, retrievers, memory, and streaming.
python langchain llm ai rag agents openai anthropic

LangSmith — LLM Observability & Evaluation

Trace, debug, evaluate, and monitor LLM applications with LangSmith. Covers tracing setup, datasets, evaluators, prompt hub, comparing runs, and CI integration.
python langsmith llm observability tracing evaluation langchain ai

LlamaIndex — Data Framework for LLMs

Build RAG pipelines and LLM-powered data applications with LlamaIndex. Covers document loading, indexing, query engines, custom LLMs and embeddings, persistent storage, and agents.
python llamaindex llm rag ai embeddings agents retrieval

ragas — RAG Evaluation Framework

Measure and improve RAG pipeline quality with ragas. Covers faithfulness, answer relevancy, context precision, context recall, dataset format, LLM judges, and CI integration.
python ragas rag evaluation llm ai retrieval metrics

transformers — Hugging Face

Load and run pre-trained models for NLP, vision, and audio with the Hugging Face Transformers library. Covers pipelines, AutoModel, tokenisation, generation, fine-tuning, and device placement.
python huggingface transformers llm nlp ml pytorch

TruLens — LLM App Evaluation

Evaluate and monitor LLM applications with TruLens. Covers the RAG triad, feedback functions, TruChain, TruLlama, custom evaluators, the dashboard, and CI integration.
python trulens rag evaluation llm ai observability feedback