Build it properly.
Engineering-grade guides for building AI agents and systems. Each one is a complete path — architecture, step-by-step build, pitfalls, and production hardening — written for teams that ship.
How to Build a Conversational Voice AI Agent
Complete guide to building a production voice AI agent that qualifies inbound leads: telephony, streaming STT/TTS, qualification logic, CRM handoff
How to Build an AI Agent That Follows Your
How to build an AI agent grounded in your organization's knowledge with retrieval-augmented generation: corpus design, chunking, hybrid retrieval
How to Add Citations to RAG Answers
Add RAG citations that cite sources at the span level: grounded answers with links, refusal when retrieval is empty, UI highlights, and faithfulness
How to Add Memory to an AI Agent
How to add memory to an AI agent or chatbot: working, session, and long-term stores, write policies, privacy, and evals for recall that do not leak PII in
How to Build a Code Review AI Agent
Build an AI code review agent for LLM pull request review: diff context, repo RAG, severity, CI comments, and evals scored against senior-reviewer
How to Build a Computer-Use Agent
How to build a computer-use agent: screenshot and DOM loop, browser sandbox, write confirmations, and evals on task success — not a scripted RPA recording.
How to Build an AI Agent with Function Calling
How to build an AI agent with tools: function-calling schemas, permissions, retries, write confirmations, and tracing that survives production traffic in
How to Build GraphRAG for Enterprise Knowledge
How to build GraphRAG: when entity graphs beat chunks, extraction, community summaries, hybrid retrieval, and evals against a document-RAG baseline in
How to Build Multimodal RAG
Build multimodal RAG for images, charts, and scanned PDFs: visual document retrieval, page-level embeddings, and citations that point at figures, not just
How to Build a Text-to-SQL Agent
How to build a text-to-SQL (NL2SQL) agent: schema packing, dialect, read-only DB user, dry-run, and evals on golden queries that catch silent wrong SQL
How to Chunk Documents for RAG
Best chunk size for RAG is measured, not guessed: structure-aware splitting, overlap, parent-child chunking, tables, and late chunking on retrieval hit
How to Deploy an LLM in a Private Cloud
How to deploy an LLM on-prem or in a private VPC with vLLM/TGI, networking, secrets, and a perimeter that keeps prompts and weights inside the network in
How to Evaluate a RAG System
How to evaluate a RAG system: golden sets, retrieval hit rate, faithfulness score, RAGAS-style metrics, LLM judges, and a CI gate that catches regressions.
How to Extract Structured Data from Documents
How to extract structured data from PDFs with AI: OCR, layout, JSON schemas, bounding-box citations, and a human review queue for invoices and contracts
How to Fine-Tune an LLM on Enterprise Data
How to fine-tune an LLM on your own enterprise data: when RAG is enough, PEFT vs full training, dataset design, evals, and a production adapter rollout in
How to Fine-Tune an LLM with LoRA
How to LoRA fine-tune an enterprise model with PEFT and QLoRA: data format, rank, merge vs serve adapters, evals, and how to limit catastrophic forgetting.
How to Implement LLM Guardrails
How to implement AI guardrails: input and output LLM safety filters, tool allow/deny policy, NeMo Guardrails, and a measured latency budget for production.
How to Prevent Prompt Injection in AI Agents
How to prevent prompt injection in AI agents: treat untrusted text as data, tool allowlists, dual-LLM checks, and evals for jailbreak attacks in
How to Red Team an AI Agent
How to red team an LLM agent: attack taxonomies, jailbreak testing, automated suites, severity scoring, and a fix loop that lands in evals before
How to Reduce LLM API Cost in Production
How to reduce LLM API cost in production: routing, prompt caching, semantic cache, smaller models, batching, and CI evals so quality does not silently
How to Reduce LLM Hallucinations in Production
How to reduce LLM hallucinations in production with grounded generation, citations, refusal, and faithfulness evals — not a prompt-only trick for ML teams.
How to Set Up LLM Observability
Set up LLM observability: trace LLM calls, monitor prompts in production, token and cost metrics, quality scores, drift alerts, and PII redaction in the
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