AI Engineering Hub

Practical guides for building production AI systems

28 in-depth guides covering RAG architecture, LLM evaluation, AI agents, vector databases, prompt engineering, cost optimization, streaming, multimodal AI, MCP, knowledge graphs, chatbot architecture, and deployment workflows.

AI engineering guides

RAG

Retrieval patterns

Agents

Tool-use & orchestration

Evals

Quality & safety gates

Core guides

RAG Architecture Guide

Build production RAG systems with embeddings, chunking, hybrid search, and vector stores.

OpenAI API Best Practices

Retry logic, streaming, token budgeting, prompt caching, and cost control for production.

AI Agent Design Patterns

ReAct loops, tool-use, memory strategies, and multi-agent orchestration for production agents.

Vector Database Comparison

Pinecone vs Weaviate vs Chroma vs pgvector — features, trade-offs, and decision framework.

LLM Fine-Tuning Guide

When to fine-tune, LoRA/QLoRA setup, data preparation, and evaluation strategies.

AI Cost Optimization

Semantic caching, model routing, Batch API, and prompt compression to cut LLM costs.

Structured Output from LLMs

JSON schema mode, function calling, Zod validation, and retry-on-failure patterns.

AI Observability & Monitoring

Trace every LLM call, track quality regression, cost, and latency in production.

Prompt Engineering Advanced

Chain-of-thought, few-shot, self-consistency, meta-prompting, and CI prompt testing.

LangChain Guide

LCEL chains, RAG pipelines, tool-using agents, memory, and streaming patterns.

AI Security & Guardrails

Defend against prompt injection, jailbreaks, and data exfiltration in LLM applications.

Semantic Search Implementation

Embeddings, cosine similarity, BM25 hybrid search, reranking, and query expansion.

AI Streaming Guide

Stream LLM responses with SSE, ReadableStream, Vercel AI SDK, and edge functions.

Multimodal AI Guide

Vision, audio transcription, TTS, and PDF processing with GPT-4o and Gemini.

Context Window Management

Sliding window, summarization, map-reduce, and lost-in-the-middle mitigation.

Embeddings Deep Dive

Model comparison, dimension reduction, batching, caching, and production patterns.

AI Testing Guide

Mock LLMs, write evals, snapshot prompts, and integrate quality checks into CI.

MCP Protocol Guide

Build MCP servers to connect AI clients to your tools, databases, and APIs.

Function Calling Deep Dive

Parallel calls, forced calling, and error handling across OpenAI, Claude, and Gemini.

Anthropic Claude API Guide

Claude messages, extended thinking, prompt caching, vision, and streaming.

AI Chatbot Architecture

Session state, context assembly, intent routing, entity memory, and fallback chains.

LLM Router & Gateway

Build an LLM gateway with routing rules, multi-provider fallbacks, and semantic caching.

AI Workflow Automation

Email triage, invoice extraction, code review bots, and document classification.

Knowledge Graphs & AI

GraphRAG, text-to-Cypher, entity extraction, and hallucination reduction with Neo4j.

Harness + AI Delivery Pipeline

Build a gated release flow from prompt change to production rollout.

LLM Evaluation Pipeline Guide

Design dataset, metrics, and scoring loops for model quality checks.

Prompt Regression Testing

Catch quality drift before deployment with repeatable evaluation suites.

AI CI/CD with Harness

Reference architecture for shipping AI apps with operational controls.