Blog
RAG, LLM cost, evaluation and the engineering behind AI products that hold up in production.
All articles
RAG development
RAG architecture: how a production pipeline fits together
How a production RAG architecture fits together: ingestion, chunking, hybrid retrieval, cited answers, fallbacks and evaluation, and where each part fails.
AI accuracy and cost audit
ChatGPT API cost: what an AI feature costs to run
What the ChatGPT, Claude and Gemini APIs cost to run in a real feature: how token pricing works, a worked monthly example, and how to keep the bill in check.
Fractional CTO or senior AI engineer
What is a fractional CTO? Role, cost and when to hire one
What a fractional CTO does, how one differs from a full-time CTO, an agency or a freelancer, what it costs, and when an AI startup should hire one.
RAG development
RAG vs fine-tuning: which one your product needs
RAG vs fine-tuning for product teams: what each one changes, when each fits, what each costs to maintain, and why most products should start with RAG.
AI accuracy and cost audit
LLM observability: what to log before users complain
LLM observability for AI features in production: what to log for every answer, how to trace why the model said it, and which cost and quality alerts matter.
AI accuracy and cost audit
RAG evaluation: how to know your answers are right
How to evaluate a RAG system: measure retrieval and answers separately, build a test set from real questions, use model judges with care, and test every change.
RAG development
pgvector vs Pinecone: choosing a vector store
pgvector vs Pinecone for RAG: vectors kept in PostgreSQL or in a managed vector database, compared on isolation, filtering, hybrid search, operations and scale.
Tell me what you’re building and where it’s stuck.
I’ll tell you the cleanest path forward, including if it’s “don’t build that.”
Or write tocontact@alihassan.dev
