Llm

Learn how to reliably extract structured data from LLMs. Covers native JSON modes, Pydantic integration, function calling, and handling edge cases.

Learn how to evaluate LLM applications effectively. Covers evaluation frameworks, metrics, test set creation, and continuous monitoring strategies.

Learn how prompt caching can reduce LLM API costs by up to 90% and improve latency. Covers implementation strategies for Anthropic, OpenAI, and custom caching solutions.

Learn how to build production-ready AI agents using Google's Agent Development Kit (ADK). Covers agent architecture, tool integration, multi-agent systems, and deployment with Vertex AI.

Explore the Model Context Protocol (MCP), an open standard for connecting AI models to external tools and data sources. Learn how to build MCP servers and integrate them with Claude and other AI systems.

Take your RAG systems to the next level with advanced techniques like query expansion, hybrid search, reranking, and sophisticated chunking strategies.

A comprehensive guide to understanding, building, and deploying AI agents. Learn about agent architectures, tool use, memory systems, and production considerations.

Explore multimodal AI models like LLaVA, GPT-4V, and Qwen-VL that understand both images and text, with practical code examples.

A practical guide to LLM quantization techniques for running large models on consumer hardware with minimal quality loss.

Learn how to run open-source LLMs locally using Ollama and vLLM for privacy, cost savings, and low-latency inference.

A practical comparison of LangChain and LlamaIndex for building LLM-powered applications with code examples for common use cases.

Master the core techniques of prompt engineering—from zero-shot to chain-of-thought—to get consistent, high-quality results from any LLM.

A comprehensive guide to building Retrieval Augmented Generation (RAG) systems that combine the power of LLMs with your own knowledge base.

Comprehensive guide to supervised fine-tuning of Large Language Models, covering data preparation, training implementation, hyperparameter optimization, and evaluation strategies with practical code examples.

Complete guide to setting up a robust development environment for LLM fine-tuning, covering hardware requirements, software installation, data preparation workflows, and optimization techniques.

A comprehensive introduction to LLM fine-tuning covering key concepts, different approaches, and guidance on choosing the right method for your use case.

Understand how LoRA enables fine-tuning of billion-parameter models on consumer GPUs by learning low-rank adaptations instead of updating all weights.

How ChatGPT works under the hood - from predicting the next word to engaging in human-like conversations. Understanding the magic behind large language models.