Tagged AI
20 articles
LLM Tool Calling for Internal Assistants: Design Choices
How to give an internal AI assistant tools — reading workspace data with the user's permissions, formatting results for the model, native function calling vs a routing step, limits on how many tools run, and answers that stay inside the data.
Building a Multilingual Chatbot That Handles Romanized Text
Real users mix languages and scripts — Nepali in Devanagari and in Latin letters, Hinglish, English with local words. How to route, retrieve, parse dates and reply in the right script, with lessons from a production assistant.
Stop Your Product Chatbot from Hallucinating Features
A help chatbot that invents buttons, settings and features is worse than no chatbot. How we fixed ours — a feature registry checked against the real UI, retrieval that finds the right page, strict grounding rules, link guards and must-not-include evals.
Caching LLM Responses Safely: What to Cache and What Never To
Caching LLM answers saves tokens and latency — and can leak one user's data to another if done carelessly. A safe design: cache only answers that are identical for everyone, key on normalized question plus content version, expire, and lock the cache down.
Intent Routing with Small LLMs: Fast, Cheap and Right
How to route chat messages to the right action or tool with a small, fast language model — a flat strict schema, deterministic argument extraction, rules before the model, a short timeout, and a 51-case evaluation — using measurements from a production assistant.
How to Reduce LLM Token Usage Without Worse Answers
Practical ways to cut LLM token usage — skip the model when rules suffice, send less context, route to smaller models, cache repeated answers, use provider prompt caching and cap output — with measured numbers from a production assistant.
AI Agent Actions with Human Confirmation: A Design Pattern
How to let an AI assistant take real actions — create tasks, request leave, book meetings — safely: propose with structured fields, show an editable confirmation card, execute the confirmed values through existing server code, and report the result.
RAG for Internal Documentation: A Small-Team Guide
How to build retrieval-augmented generation over your internal docs without a big platform — structuring documents for retrieval, hybrid search, small prompts, grounding rules, permissions, and measuring whether answers are right.
Prompt Injection in Internal AI Tools: A Practical Defense
Internal AI assistants read tickets, documents and messages written by other people — which makes prompt injection an internal threat too. How direct and indirect injection work, and the layered defenses that actually limit the damage.
How to Evaluate an LLM Feature with a Golden Set
A practical way to test LLM features before shipping changes — a golden set of real inputs, deterministic checks for what must and must not appear, separate evals for retrieval, routing and answers, and running them on free-tier rate limits.
Generative Engine Optimization (GEO): What Actually Works
How to get cited by AI search — Google AI Overviews, ChatGPT search, Perplexity — based on what the research and the platforms' own documentation say, not guesses. Crawlers, content, structure, and what doesn't work.
A Free AI Gateway: Gemini + Groq Failover That Stays Fast
How to put several free AI providers behind one function — per-use-case model chains, cooldowns on 429s and timeouts, no interactive retries, and the measurements that showed why each rule exists.
Semantic Search for a Knowledge Base with Supabase pgvector
Build semantic search over a help desk knowledge base with Supabase and pgvector — embeddings, an HNSW index, a similarity threshold, hybrid keyword search, and the multi-tenant security mistake to avoid.
Ticket Deflection with a Self-Service Knowledge Base
Ticket deflection means answering a question before it becomes a ticket. How to build a knowledge base people actually find — suggestions while they type, semantic search, articles written from real requests — and how to measure deflection honestly.
Streaming AI Responses in Next.js: Plain Streams vs SSE
How to stream LLM answers from a Next.js Route Handler to the browser — a plain ReadableStream or Server-Sent Events, reading it with fetch, saving before the stream closes, and falling back when the model fails mid-answer.
AI Request Classification with Structured Output
How to classify help desk requests with an LLM using structured JSON output — schema design, validation, confidence thresholds, free-tier limits and the fallback path — so a wrong or missing answer never breaks intake.
Support Ticket Routing: Getting Requests to the Right Team
Every misrouted ticket waits twice. How to route requests to the right team — requester choice, rules, AI suggestions — how to measure routing quality, and what to do when it fails.
How AI Is Changing IT Service Management (and What It Can't Do)
Where AI genuinely helps IT service management today — intake, triage, knowledge, drafting, assistants that act with confirmation — where it doesn't, and how to adopt it without betting your service desk on a model.
How to Track Your Team's AI and API Spend
AI and API costs sprawl across teams and cards until an invoice surprises you. A lightweight way to track spend — an inventory, budgets, a usage ledger, automatic sync where providers allow it, and alerts before renewals and overruns.
The Case for AI That Degrades Gracefully
AI features fail — rate limits, timeouts, outages, wrong answers. Design so the product still works when they do: save first, suggest don't decide, fall back to rules and cached answers, and tell users plainly.