Blog
AI engineering insights, practical advice, and things I'm learning.
AI Engineering
How Function Calling Works in LLMs
Function calling lets LLMs interact with external systems by requesting structured tool executions. Here's how the loop works, how to define tools, and what to watch for across providers.
Function Calling · Tool Use · Llms
AI Engineering
How to Stream LLM Responses in Your Application
Streaming LLM responses reduces perceived latency and improves UX. Here's how server-sent events work, how to implement streaming with OpenAI and Anthropic, and what to watch for in production.
Streaming · Llms · Server Sent Events
AI Engineering
How to Evaluate AI Output (LLM-as-Judge Explained)
Traditional tests don't work for AI output. Here's how to evaluate quality using LLM-as-judge, automated checks, human review, and continuous evaluation frameworks.
Evaluation · Llm As Judge · Ai Engineering
AI Engineering
How to Run IBM Granite 4.0 1B Speech for Multilingual Edge ASR and Translation
Learn how to deploy IBM Granite 4.0 1B Speech for fast multilingual ASR and translation on edge devices.
Speech Models · Edge Ai · Multilingual Asr
AI Engineering
Context Engineering: The Most Important AI Skill in 2026
Context engineering is replacing prompt engineering as the critical AI skill. Learn what it is, why it matters more than prompting, and how to manage state, memory, and information flow in AI systems.
Context Engineering · Prompt Engineering · Rag
AI Engineering
How to Choose a Vector Database in 2026
Pinecone, Weaviate, Qdrant, pgvector, or Chroma? Here's how to pick the right vector database for your AI application based on scale, infrastructure, and actual needs.
Vector Database · Embeddings · Rag
AI Engineering
GPT vs Claude vs Gemini: Which AI Model Should You Use?
A practical comparison of GPT, Claude, and Gemini. Their real strengths, pricing, context windows, and which model fits which task in 2026.
Gpt · Claude · Gemini
AI Engineering
AI Agent Frameworks Compared: LangChain vs CrewAI vs LlamaIndex
A practical comparison of the top AI agent frameworks in 2026. When to use LangChain, CrewAI, or LlamaIndex, their strengths, tradeoffs, and what actually works in production.
Langchain · Crewai · Llamaindex
AI Engineering
How to Build a RAG Application (Step by Step)
A practical walkthrough of building a RAG pipeline from scratch: chunking documents, generating embeddings, storing vectors, retrieving context, and generating grounded answers.
Rag · Retrieval Augmented Generation · Embeddings
AI Engineering
How to Run LLMs Locally on Your Machine
Running AI models locally gives you privacy, speed, and zero API costs. Here's what hardware you need, which tools to use, and how to choose the right model.
Local Llms · Ollama · Llama
AI Engineering
Structured Output from LLMs: JSON Mode Explained
LLMs generate text, but applications need structured data. Here's how JSON mode, function calling, and schema enforcement turn free-form AI output into reliable, typed data.
Structured Output · Json Mode · Function Calling
AI Engineering
Fine-Tuning vs RAG: When to Use Each Approach
RAG changes what the model knows. Fine-tuning changes how it behaves. Here's when to use each approach, their real tradeoffs, and why the answer is usually both.
Fine Tuning · Rag · Llm
AI Engineering
What Is the Model Context Protocol (MCP)?
MCP standardizes how AI models connect to tools and data. Here's what the Model Context Protocol is, how it works, and why it matters for developers building AI applications.
Mcp · Model Context Protocol · Ai Agents
AI Engineering
What Is RAG? Retrieval-Augmented Generation Explained
RAG lets AI models pull in real data before generating a response. Here's how retrieval-augmented generation works, why it matters, and where it breaks down.
Rag · Retrieval Augmented Generation · Llms
AI Engineering
What Are Embeddings in AI? A Technical Explanation
Embeddings turn text into numbers that capture meaning. Here's how they work, why they matter for search and RAG, and how to choose the right model for your use case.
Embeddings · Vector Search · Ai Architecture
AI Engineering
Why AI Hallucinates and How to Reduce It
AI hallucination isn't a bug you can patch. It's a consequence of how language models work. Here's what causes it, how to measure it, and what actually reduces it.
Hallucination · Llms · Ai Safety
AI Engineering
What Is AI Temperature and How Does It Affect Output?
Temperature controls how random or deterministic an AI model's output is. Here's what it does technically, how it relates to top-p and top-k, and when to adjust it.
Temperature · Llm · Ai Engineering
AI Engineering
Context Windows Explained: Why Your AI Forgets
Context windows determine how much an AI model can 'see' at once. Here's what they are technically, how attention scales, and practical strategies for working within their limits.
Context Windows · Llms · Prompt Engineering
AI Engineering
What Is an LLM? How Large Language Models Actually Work
LLMs predict text, they don't understand it. Here's how large language models work under the hood, from training to transformers to next-token prediction, and why it matters for how you use them.
Llm · Large Language Models · Ai Engineering
AI Engineering
What Tokenization Means for Your Prompts
Tokenization isn't just a technical detail. It shapes how LLMs process your input. Understanding it changes the way you write prompts.
Tokenization · Llms · Prompt Engineering