Guides
Practical AI knowledge that compounds
Deep guides for developers, researchers, and teams building real AI systems.
LLM hallucination: causes, detection, and mitigation (2026)
What LLM hallucination actually is, why it happens, how to detect it, and the practical patterns teams use to reduce it in production systems.
10 min read
Verified 2026-04-13
Token limits and context windows: how to manage them effectively (2026)
What tokens actually are, how context windows behave in production, and the practical patterns teams use to manage long prompts, RAG pipelines, and agent loops.
10 min read
Verified 2026-04-13
AI evaluation frameworks: RAGAS, DeepEval, and PromptFoo compared (2026)
How to evaluate LLM applications in production — what RAGAS, DeepEval, and PromptFoo measure, how they differ, and how to choose the right eval framework for your stack.
11 min read
Verified 2026-04-12
Multimodal AI: working with vision, audio and documents (2026)
How to use vision, audio, and document inputs with LLMs — practical patterns for image understanding, audio transcription, PDF parsing, and building multimodal pipelines in production.
10 min read
Verified 2026-04-12
Running LLMs locally: Ollama, vLLM, and LM Studio (2026)
When and why to run LLMs on your own hardware, how Ollama, vLLM, and LM Studio compare, and what it takes to get a local model into production.
10 min read
Verified 2026-04-12
Semantic search vs keyword search: when to use each (2026)
How BM25 and vector search actually work, where each one fails, why hybrid search usually wins in production, and how to decide which approach fits your use case.
10 min read
Verified 2026-04-12
Structured output: getting reliable JSON from any LLM (2026)
Why structured outputs matter, how JSON mode and schema enforcement differ, and practical patterns for getting reliable JSON from LLMs in production.
11 min read
Verified 2026-04-12
How to write a great system prompt (2026)
What system prompts actually do, why they break, and the patterns that make them reliable in production — with examples for assistants, extractors, and agents.
10 min read
Verified 2026-04-12
Vector databases compared: Pinecone vs Weaviate vs pgvector vs Chroma (2026)
What vector databases do, how ANN indexing works, and how to choose between Pinecone, Weaviate, pgvector, and Chroma for RAG and production retrieval.
11 min read
Verified 2026-04-12
Where AI doesn't exist yet: the gaps MIT's research exposed
MIT mapped 39,603 work activities against every AI tool ever built. Most of the map is empty. Here's what's in the white space — and why it matters for the next wave of AI products.
12 min read
Verified 2026-03-31
Context windows explained: how to use them effectively (2026)
What context windows are, why they matter for performance and cost, and practical strategies for long documents, agent loops, and production AI apps.
10 min read
Verified 2026-03-31
MIT mapped every AI app against every job. Here's what they found.
Researchers at MIT mapped 39,603 work activities against 13,275 AI tools and every industrial robot ever deployed. The result is the most comprehensive picture of where AI is — and isn't — going.
14 min read
Verified 2026-03-31
How LLMs actually work: transformers, tokens, and attention explained (2026)
A practical, deep explanation of how large language models work — covering transformers, tokenisation, attention mechanisms, training, and what this means for builders.
20 min read
Verified 2026-03-30
AI security guide: prompt injection, jailbreaking, and PII protection (2026)
A practical guide to AI security for builders. Covers prompt injection, jailbreaking, PII leakage, RAG poisoning, and a 20-point production security checklist.
14 min read
Verified 2026-03-21
Build your first AI agent: step-by-step tutorial with LangGraph (2026)
A complete tutorial for building your first AI agent in 2026 using LangGraph and Claude. Covers tools, memory, human-in-the-loop, and a full working research assistant.
18 min read
Verified 2026-03-21
Embeddings explained: how they work and which to use in 2026
A practical guide to embeddings for AI builders. Covers how embeddings work, the best models in 2026, and working Python code for generating and searching embeddings.
13 min read
Verified 2026-03-21
Fine-tuning LLMs: complete guide to LoRA, QLoRA, and when to fine-tune (2026)
A practical guide to fine-tuning large language models in 2026. Covers LoRA, QLoRA, dataset creation, and an honest framework for when fine-tuning beats RAG.
16 min read
Verified 2026-03-21
LLMOps guide: how to monitor, debug and evaluate AI in production (2026)
A practical guide to LLMOps in 2026. Covers observability, prompt testing, cost monitoring, evaluation, and the best tools for running AI in production.
15 min read
Verified 2026-03-21
AI agent frameworks compared: LangGraph vs CrewAI vs AutoGen (2026)
An honest comparison of the top AI agent frameworks in 2026. Covers LangGraph, CrewAI, AutoGen, and OpenAI Agents SDK with code examples and a clear decision framework.
14 min read
Verified 2026-03-19
How to build a RAG system from scratch (2026 guide)
A complete, practical guide to building production-ready RAG systems. Covers chunking, embeddings, vector databases, retrieval, and evaluation with working Python code.
20 min read
Verified 2026-03-19
LLM cost guide: how to choose the right AI model for your budget (2026)
A practical guide to LLM pricing in 2026. Compare GPT-5.4, Claude, and Gemini costs and learn 10 ways to reduce your AI API spend.
12 min read
Verified 2026-03-19
GPT-5.4 vs Claude Sonnet 4.6 vs Gemini 3.1 Pro: honest comparison (March 2026)
An honest, up-to-date comparison of the best AI models in 2026. Covers pricing, strengths, weaknesses, and which model to use for your specific task.
15 min read
Verified 2026-03-19
The complete prompt engineering guide (2026)
The most comprehensive, practical prompt engineering guide covering zero-shot, few-shot, chain-of-thought, system prompts, and advanced techniques with real examples.
18 min read
Verified 2026-03-19