FROM CHAT TO STACK: PRACTICAL AI PATTERNS BACKEND TEAMS CAN SHIP NOW
Developers are converging on three AI primitives—completions, embeddings, and tool use—to ship production features and automation faster. A hands-on guide brea...
Developers are converging on three AI primitives—completions, embeddings, and tool use—to ship production features and automation faster.
A hands-on guide breaks down how to treat LLM completions, embeddings, and function-calling as first-class building blocks, with concrete prompts and structured outputs beyond chat UIs Beyond the Chatbot. The focus: retrieval, reasoning, and automation patterns you can drop into existing services.
On the product side, a Next.js 14 boilerplate with Supabase let one developer ship five niche CRM templates in days, using AI to draft domain-specific SQL migrations and page scaffolds 5 CRM Templates. It highlights how a stable core plus AI cuts the long tail of CRUD and theming.
Another portfolio shows a practical stack for AI automation: Next.js on Vercel, Docker, Python scripts, and n8n to orchestrate agents and workflows AI Automation Portfolio. Together, these pieces outline a repeatable path from prototype to production.
Standardizing on a small set of AI primitives reduces risk and time-to-value for internal tools and data workflows.
A stable web/data stack plus AI assistance shortens schema, scaffolding, and orchestration work by an order of magnitude.
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terminal
Run a small RAG spike over your internal docs: compare embedding models, chunking, and top-k settings for accuracy, latency, and cost.
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terminal
Evaluate function-calling reliability: enforce JSON response formats on 100+ structured prompts and track tool-call error rates and recovery paths.
Legacy codebase integration strategies...
- 01.
Wrap existing microservices and data fetchers as callable tools/functions for the model; gate side effects and add audit logs.
- 02.
Index runbooks, schemas, and API docs into a vector store to power retrieval, but cache hot queries and set strict token/latency budgets.
Fresh architecture paradigms...
- 01.
Design an AI gateway early (prompt templates, tool registry, safety, observability) and make it language/runtime agnostic.
- 02.
Pick a boring default: one completion model, one embedding model, one vector store; swap models behind the gateway once baselines are stable.