Technical Deep-Dives
Architecture decisions, model trade-offs, and production lessons from building AI products. Written by the engineers who shipped them.
16 articles
LangGraph vs LangChain in Production: When Each Makes Sense
Eight projects started in LangChain, four got rewritten to LangGraph. Failure modes that drove those rewrites and the decision matrix we use today.
Anil Gulecha
LLM Structured Output: JSON Mode vs Function Calling
JSON mode, function calling, and Pydantic tool use compared: failure rates, latency costs, and when each method actually holds in production AI systems.
Anil Gulecha
Model Cost Optimization: Cut LLM Bills 80% in Production
How to cut LLM API costs by 80% without degrading quality. Model routing, prompt compression, caching, and batching patterns from production systems.
Anil Gulecha
Agentic AI in Production: Tool-Calling, Planning, Recovery
Tool schema design, planning loop limits, and error recovery patterns for production AI agents. Patterns from six deployed agentic systems.
Anil Gulecha
LLM Guardrails That Actually Work in Production
Input validation, output filtering, and containment patterns for LLM apps. What breaks, what holds, and what we stopped using.
Anil Gulecha
Production AI on Cloudflare Workers: Architecture Guide
How to architect AI inference, RAG pipelines, and agent workflows on Cloudflare Workers. Cold starts, CPU limits, streaming, and real tradeoffs.
Anil Gulecha
AI Evaluation Pipelines: Testing Your Model in Production
How to build AI evaluation pipelines: offline test suites, online monitoring, LLM-as-a-judge, and the metrics that actually matter in production.
Anil Gulecha
Fine-Tuning vs RAG vs Prompt Engineering: When to Use What
When to use fine-tuning vs RAG vs prompt engineering in production. Decision framework, cost data, and real examples from 11 AI projects.
Anil Gulecha
Prompt Engineering Is Dead. Prompt Architecture Matters.
Stop tweaking individual prompts. Production AI needs prompt architecture: routing, decomposition, and template systems that scale across models.
Anil Gulecha
Vector Databases Compared: pgvector vs Pinecone vs Qdrant vs Weaviate
Real benchmarks, operational trade-offs, and code examples for pgvector, Pinecone, Qdrant, and Weaviate. Which vector DB to use and when.
Anil Gulecha
Vibe Coding in Production: How We Use AI to Build AI
Our team ships AI products using AI coding tools every day. Here's what actually works, what breaks, and the workflows we've settled on after 6 months.
Abraham Jeron
LLM Selection for Production: GPT-4o vs Claude vs Gemini
How we pick LLMs for production systems. Cost benchmarks, latency data, structured output reliability, and when open source beats commercial.
Anil Gulecha
Building AI Products for Startups: Decision Framework
When to build AI features, when not to. Build vs buy, model selection, RAG vs agents. A technical decision framework for startup CTOs at seed and Series A.
Anil Gulecha
AI Chatbot Development: Beyond 'Just Add ChatGPT'
Most AI chatbots fail because they're built like demos, not products. Here's what actually goes into a chatbot that users trust: from RAG architecture to guardrails to the evaluation pipeline you're probably skipping.
Abraham Jeron
Building AI Agents: Architecture, Trade-offs, and What We've Learned
A technical deep-dive into how we architect AI agents for production. LangChain vs custom, model selection, tool-calling patterns, and the mistakes that cost us time.
Anil Gulecha
RAG in Production: What Works, What Doesn't, and Why We Stopped Using Pinecone
What we've learned building RAG systems for clients: embedding models, chunking strategies, retrieval accuracy, and why pgvector beat Pinecone for most of our use cases.
Anil Gulecha