Production Engineering Insights
Evidence-backed articles on what it takes to make AI work in production.
Business Value
Where Enterprise Knowledge Assistants Create Measurable Value
How knowledge assistants create value across support, operations, compliance and research.
Read more →Architecture
RAG, Fine-Tuning or Workflow Logic: Choosing the Right Approach
When each approach fits, and how to avoid over-engineering a solution.
Read more →Business Value
How to Choose a Business Workflow for AI Automation
Which workflows are suitable for AI, and which are better left deterministic.
Read more →Production Engineering
What an AI MVP Needs Before Production
The gap between a working prototype and a live system, and what to close first.
Read more →Deployment & Economics
Measure Cost per Completed Workflow, Not Cost per Model Call
The real drivers of inference cost and the engineering that reduces it.
Read more →Production Engineering
How to Improve LLM Response Time Without Losing Quality
Serving, batching, caching and routing strategies that reduce latency safely.
Read more →Safety
Guardrails for AI Systems That Use Tools and Take Actions
Safety for systems that interact with untrusted content and take real actions.
Read more →Safety
Prompt-Injection Controls for AI That Reads Files
How to prevent malicious file content from hijacking an assistant's behavior.
Read more →Deployment & Economics
Cloud, Private or Hybrid AI: A Decision Framework
A decision framework based on data, latency, cost and control.
Read more →Architecture
Why Deterministic Steps Make AI Workflows More Reliable
Where rules beat reasoning, and how to tell the difference.
Read more →Architecture
Predictive AI or Generative AI: Which Fits the Decision?
How to choose between predictive and generative AI for a given business decision.
Read more →Production Engineering
How to Evaluate an Enterprise RAG System
Retrieval quality, answer correctness and the measures that matter.
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