Guides

Understand how AI systems create new text, images, code, and more from learned patterns.

Learn how LLMs like GPT, Claude, and Llama process and generate human language at scale.

Discover how AI converts text, images, and data into numerical vectors that capture meaning.

Understand the basic units that LLMs read and write - and why they matter for cost and performance.

Learn how to adapt a pre-trained model to your specific domain or task with custom training data.

Understand the transformer architecture that powers every modern LLM - self-attention, encoder-decoder stacks, and why it replaced RNNs.

Learn how attention lets models focus on relevant parts of input - the core mechanism behind GPT, Claude, and every modern LLM.

Low-Rank Adaptation - the most popular parameter-efficient fine-tuning method for adapting LLMs without full retraining.

Quantized LoRA - fine-tune large models on consumer GPUs by combining 4-bit quantization with low-rank adapters.

Parameter-Efficient Fine-Tuning - techniques to adapt large models by training only a small subset of parameters.

Reinforcement Learning from Human Feedback - how ChatGPT and Claude learn to be helpful, harmless, and honest.

Direct Preference Optimization - a simpler alternative to RLHF that aligns models using preference pairs without a separate reward model.

Explore how AI agents autonomously reason, plan, and take actions to accomplish goals.

Understand the paradigm shift from prompt-response AI to systems that act autonomously.

Learn how multiple AI agents collaborate, delegate, and coordinate to solve complex problems.

ReAct, Plan-and-Execute, reflexion, and other architectural patterns for building reliable AI agents.

How AI agents decompose goals into steps, create plans, and adapt when things go wrong.

Short-term, long-term, and episodic memory patterns for AI agents that need to remember across sessions.

RAG systems where agents decide what to retrieve, when to search, and how to synthesize multi-step answers.

When to use deterministic LLM workflows vs autonomous agents - and why most production systems are hybrids.

Master the art of crafting effective prompts to get better, more reliable results from LLMs.

Learn about MCP - the open standard for connecting LLMs to external tools and data sources.

Understand how LLMs invoke external tools and APIs to extend their capabilities beyond text generation.

Learn how LLMs generate structured function calls that your application can execute.

Understand the input size limits of LLMs and strategies for working within them.

Learn how to get LLMs to return data in reliable formats like JSON, XML, or typed schemas.

OpenAI GPT family - architecture evolution, model tiers, API capabilities, and production considerations.

Anthropic Claude family - extended context, constitutional AI, tool use, and when to choose Claude over GPT.

Google Gemini models - multimodal capabilities, long context, and integration with the Google ecosystem.

Meta Llama open-weight models - self-hosting, fine-tuning, quantization, and building on open foundations.

Mistral AI models - efficient architectures, Mixtral MoE, and strong open-weight alternatives.

DeepSeek models - reasoning-focused architectures, open weights, and cost-efficient inference.

A comprehensive guide to RAG - the dominant pattern for building AI applications that answer questions using your own data.

Combine keyword and semantic search for more accurate and comprehensive information retrieval.

Learn how AI understands the meaning behind queries to find relevant results beyond keyword matching.

Understand how vector databases find similar items using high-dimensional embedding comparisons.

Purpose-built databases for storing, indexing, and querying embedding vectors at scale.

How to split documents for RAG - fixed-size, recursive, semantic, and document-aware chunking with production trade-offs.

Choosing and evaluating embedding models - OpenAI, Cohere, BGE, E5, and open-source alternatives for production RAG.

Cross-encoder rerankers that re-score retrieved documents for precision - the highest-ROI improvement in most RAG pipelines.

Pre-filter documents by metadata before vector search - tenant isolation, date ranges, document types, and access control.

Measuring retrieval quality - recall@k, MRR, nDCG, and building golden test sets for RAG pipelines.

Structured representations of entities and relationships - the foundation for GraphRAG, enterprise search, and neuro-symbolic AI.

Understand what knowledge graphs are, how they represent real-world entities and relationships, and why they matter for AI.

Labeled property graph model - nodes and edges with key-value properties, used by Neo4j and most graph databases.

The declarative query language for property graphs - pattern matching, traversals, and Neo4j queries.

Neo4j, Amazon Neptune, TigerGraph, and RDF stores - choosing and operating graph databases in production.

Compare the two dominant knowledge graph models - RDF (W3C standard) and property graphs (Neo4j-style) - and when to use each.

Learn about the Resource Description Framework - the standard for representing knowledge as linked data.

Master the query language for RDF knowledge graphs and linked data endpoints.

Understand how ontologies define the vocabulary, relationships, and rules for a domain of knowledge.

Learn how SHACL (Shapes Constraint Language) validates and constrains RDF data against defined shapes.

Explore the Web Ontology Language for defining rich, machine-interpretable ontologies with reasoning capabilities.

Learn how organizations use knowledge graphs to connect data silos and power intelligent applications.

Explore how knowledge graphs enhance RAG pipelines with structured relationships and reasoning.

Discover how knowledge graphs and large language models complement each other - grounding LLMs with structured knowledge.

Practical guidelines for designing, building, and maintaining knowledge graphs in production.

Learn about Extract, Transform, Load - the foundational pattern for moving and preparing data.

Understand centralized repositories that store raw data at any scale for analytics and AI.

Learn how data warehouses organize and optimize structured data for analytics and reporting.

Explore the lakehouse architecture that combines the best of data lakes and data warehouses.

Measuring LLM output quality - automated metrics, human eval, LLM-as-judge, and building eval pipelines.

Evaluating RAG systems - faithfulness, answer relevance, context precision, and frameworks like RAGAS.

MMLU, HumanEval, MTEB, and domain benchmarks - what they measure and how to use them without gaming metrics.

Detecting when LLMs fabricate facts - grounding checks, NLI verification, citation validation, and production guardrails.

Systematically testing and comparing prompts - A/B testing, prompt versioning, and regression detection.

Learn how to measure and improve the quality, accuracy, and reliability of AI systems.

Understand how to monitor, trace, and debug AI systems in production.

Learn how to add safety constraints and validation to AI outputs to prevent harmful or incorrect results.

Understand why LLMs sometimes generate confident but incorrect information, and how to mitigate it.

Learn about threats to AI systems - prompt injection, data poisoning, and model attacks - and how to defend against them.

Designing production AI systems - orchestration layers, model routing, retrieval pipelines, and failure domains.

Reducing LLM API costs - model routing, prompt compression, caching, batching, and smart token management.

Making AI systems fast - streaming, parallel retrieval, model selection, edge caching, and speculative decoding.

Caching LLM responses and embeddings - exact match, TTL strategies, and when caching helps vs hurts.

Cache LLM responses by semantic similarity - serving cached answers for paraphrased queries without exact string matches.