RAG That Doesn't Hallucinate: 12 Hard-Won Production Patterns
Chunking, hybrid retrieval, reranking, citation enforcement, and eval — the patterns that took our enterprise RAG systems from 70% to 95% groundedness.
RAGLLMEvaluationProduction
What's inside
- 1Why naive cosine retrieval is the wrong default for enterprise corpora
- 2Hybrid retrieval (BM25 + dense) and the rerankers that actually help
- 3Chunking strategies for contracts, knowledge bases, and code
- 4Citation enforcement and refusal: making the model say 'I don't know'
- 5Eval harnesses: golden sets, faithfulness, answer relevance
- 6Closing the loop: production telemetry → retraining signal
Agentic AI Without the Runaway Loop
Tool selection, planning budgets, deterministic guardrails, and the observability that turns agents from a demo gimmick into a reliable enterprise capability.
AI agentsTool useObservability
What's inside
- 1Why most 'agent frameworks' fail past three tools
- 2Planning budgets, step caps, and self-critique loops
- 3Deterministic guardrails: schemas, allowlists, and dry-runs
- 4Observability: traces, replays, and failure clustering
- 5When NOT to use an agent (and what to use instead)
MLOps Platform: Build, Buy, or Assemble? A 2026 Decision Tree
When SageMaker / Vertex / Azure ML earn their licence cost, when open source wins, and the assembled stack patterns we ship for mid-size enterprises.
MLOpsPlatformAWSAzure
What's inside
- 1The four decisions that drive build/buy/assemble
- 2Feature stores: when you actually need one
- 3Model registry, CI/CD, and the deployment ladder
- 4Eval harness as a first-class platform component
- 5Multi-tenant, multi-team isolation patterns
AI Governance: Ship Faster With Your Auditors, Not Around Them
The governance artefacts (model cards, prompt registries, eval reports, DPIAs) that turn audit from a 6-week blocker into a 1-week confirmation.
AI governanceComplianceRisk
What's inside
- 1What auditors actually want to see in 2026
- 2Model cards, prompt registries, and reproducibility
- 3PII redaction, tenant isolation, and zero-retention contracts
- 4DPIAs and EU AI Act readiness without the consulting bill
- 5Operating the AI governance board (and not stalling delivery)
Why Document Intelligence Now Beats RPA on 80% of Form-Heavy Work
The economics flipped in 2024–2025. Here's how Azure Form Recognizer, custom extraction, and validation pipelines retire entire RPA estates — with real metrics.
Document AIOCRAutomation
What's inside
- 1Why RPA's brittle UI scraping is now the more expensive option
- 2Hybrid extraction: layout models + LLM normalization
- 3Validation, confidence routing, and the human review surface
- 4STP rate, error rate, and cycle time: the metrics that matter
- 5Migration path from an RPA estate to document AI
