AI Engineering Knowledge Base
Deep dives into the most critical questions in MLOps, RAG, and LLM development.
Vector Databases & Embeddings: A Practical Guide for RAG, Search, and AI Apps
Learn what embeddings are, how vector databases work, how to design chunking + indexing, and how to evaluate retrieval quality in production.
Prompt Engineering & Optimization: Patterns, Anti-Patterns, and Proven Workflows
Design prompts that are reliable, steerable, and measurable. Covers structure, context packing, constraints, few-shot, tool-use, evaluation, and iterative optimization with clear good vs bad examples.
AI Agent Tool Integration & Function Calling: Design, Contracts, and Safety
How to wire tools into agents safely and reliably: function schemas, argument validation, tool routing, retries, observability, and evaluation—plus clear examples.
Training Data Pipelines & ETL: Collect, Clean, Label, and Ship
Design reliable pipelines for LLM training data: sourcing, PII scrubbing, deduplication, normalization, labeling, quality checks, and dataset versioning.
LLM Fine-Tuning Best Practices & Techniques (LoRA, QLoRA, SFT, DPO)
A practical, end-to-end guide to fine-tuning LLMs: choosing LoRA vs QLoRA vs full tuning, data formatting, evals, costs, and deployment pitfalls.
RAG vs. Fine-tuning: When should I use which?
The definitive guide to choosing between Retrieval-Augmented Generation and Fine-tuning for your LLM application.
How to evaluate and benchmark RAG pipelines effectively?
Stop guessing. Learn how to use LLM-as-a-Judge frameworks to quantitatively measure your RAG performance.
How to reduce LLM inference latency and token costs?
The pain everyone eventually hits but nobody budgets for: LLM unit economics. Learn how to reduce costs and latency without gutting quality.
Best practices for building and orchestrating Multi-Agent Systems?
Moving beyond chains: How to manage state, memory, and collaboration in agentic workflows with LangGraph and AutoGen.
How to run high-performance LLMs locally?
Keep your data private and reduce cloud bills by hosting Llama 3, Mistral, or Gemma on your own infrastructure with Ollama, llama.cpp, and vLLM.
How to secure LLMs against prompt injection and jailbreaking?
Protecting your GenAI application from adversarial attacks and malicious inputs.
What is the best Vector Database for scale and hybrid search?
Navigating the crowded vector database market: Dedicated vs. Integrated solutions.
How to implement effective LLM observability and tracing?
Opening the black box: How to debug complex chains and monitor production performance.
Small Language Models (SLMs) vs. Large Language Models (LLMs)
Do you really need 70B parameters for every task? How small and tiny models let you hit your latency and cost goals without giving up reliability.
How to handle long context windows vs. retrieval strategies?
With 1M+ token windows, is RAG dead? Understanding the 'Lost in the Middle' phenomenon.
Multi-Agent Systems & Agentic AI: From Hype to Reliable Operations
How to monitor, analyze, and continuously fine-tune multi-agent and agentic AI systems in production using deep observability and feedback loops.
Data Labeling & Dataset Quality: The Foundation of Reliable LLM Fine-Tuning
Model size matters, but your labels matter more. Learn how to design high-quality datasets and labeling workflows that make fine-tuned LLMs and production agents actually reliable.
GraphRAG & Advanced RAG Techniques: When Plain Vector Search Isn’t Enough
Go beyond basic vector search: how GraphRAG, multi-hop retrieval, and graph-aware prompts unlock deeper reasoning in complex domains—and how to evaluate and operate them in production.
Flagship LLMs in 2025: How to Choose and Operate GPT-4o, Claude, Gemini & Beyond
Frontier models are powerful—but they’re not free. Learn when you really need GPT-4o/Claude/Gemini-class models, when smaller models are enough, and how to operate a multi-model stack with proper monitoring and evaluation.
Open-Source LLMs in 2025: Llama, Mistral, Qwen, Gemma & Friends
Llama, Mistral, Qwen, Gemma and other open models have changed how teams think about cost, privacy, and customization. Learn when to choose open-source LLMs, how they compare, and how to fine-tune and operate them with confidence.
LLM Regression Testing & CI: Shipping Model Changes Without Fear
Models, prompts, and pipelines change constantly. Learn how to build LLM regression suites, wire them into CI/CD, and use production traces to catch regressions before they hit users.