Learn how to build modular LangGraph apps with subgraphs you can develop, test, and reuse across projects — with full code examples and patterns.
Build multi-agent AI systems in LangGraph using supervisor, swarm, and network patterns — with full code for each and a guide to choosing the...
Save and resume LangGraph agent state with checkpointers so your conversations survive crashes, restarts, and long breaks between sessions.
Make your LangGraph agents production-ready with retry logic, fallback paths, and error tracking that keeps pipelines alive when things go wrong.
Start using the OpenAI API Python SDK today. Build your first LLM-powered app with runnable code for chat completions, streaming, and token management.
Follow this hands-on LangChain tutorial to master chat models, prompt templates, output parsers, and LCEL chains with runnable Python examples.
Apply prompt engineering fundamentals — zero-shot, few-shot, chain-of-thought, and structured output — to get consistent, reliable results from any LLM.
Learn to build a ReAct agent from scratch in LangGraph with this hands-on guide — wire the think-act-observe loop, add tools, and debug agent...
Build LangGraph agents that call tools with @tool, bind_tools, and ToolNode. Step-by-step examples for web search, database queries, and API calls in Python.
Understand LangGraph nodes edges state and conditional routing through visual diagrams and runnable Python examples you can copy and extend.
Build LangGraph workflows that branch at runtime using conditional edges and routing functions. Route by LLM output, user input, or custom logic with examples.
Discover what is LangGraph, how it compares to LangChain, and when graph-based orchestration is the right choice for building reliable AI agents.
Master LangGraph state management with TypedDict schemas, reducer functions, and add_messages. Learn how nodes share data, merge updates, and track message history.
Complete your LangGraph installation setup in minutes, then build and run your first StateGraph that calls an LLM and returns a response.
Evaluate your LLM using MMLU, MT-Bench, LLM-as-judge, and ROUGE. Covers lm-evaluation-harness, fine-tuned model comparison, and evaluation pitfalls. With code.
Fine-tune LLMs with Unsloth — 2x faster training, 70% less GPU memory. Step-by-step guide with LoRA, QLoRA, code examples, and deployment tips.
Learn LLM evaluation from scratch -- benchmarks, metrics (BLEU, ROUGE, perplexity), LLM-as-judge, and custom pipelines with runnable Python code.
Fine-tune LLMs with LoRA and QLoRA in Python. Complete guide covering memory math, PEFT setup, 4-bit QLoRA, adapter merging, and common mistakes — with...
Align LLMs with human preferences using one loss function -- no reward model, no RL. Complete guide with derivation, PyTorch code, and DPO variants.
Learn how to build a custom instruction dataset for LLM fine-tuning — covering Alpaca, ShareGPT, and DPO formats, quality filtering, synthetic data generation, token...
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