Retrieval-augmented generation: architecture that holds up in production
A working retrieval pipeline is straightforward. Making it reliable enough to trust with business questions is where the engineering is.
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AI & Data
Every article we have published on ai & data.
AI & Data
AI & Data
Retrieval is not enough: what makes an enterprise AI assistant reliable
Connecting a language model to your documents is the straightforward part. The reliability work is everything after that, and it looks nothing like the demonstration.
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AI & Data
Evaluating LLM systems: moving beyond it looks about right
Without an evaluation set, every change to a language model system is argued on anecdote. Building one is the highest-value early task.
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AI & Data
AI governance: documentation and controls that satisfy an auditor
Governance is largely about being able to explain what a system does, on what data, with what oversight, and to evidence it.
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AI & Data
Automating document processing without losing accuracy
Extracting structured data from real-world documents is achievable. The engineering is in confidence thresholds and review design.
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AI & Data
MLOps fundamentals: getting a model from notebook to production
Most models that fail in production fail operationally rather than statistically. Reproducibility, deployment and monitoring are the gap.
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AI & Data
Time series forecasting for business planning
Forecasting adds value when planners act on it. That depends more on explaining uncertainty than on squeezing out accuracy.
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AI & Data
Choosing AI use cases worth building
Most failed AI projects were poor candidates before any code was written. A short triage saves considerable money.
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AI & Data
Prompt engineering for production systems, not demos
Prompts in production are code: versioned, tested and monitored. Treating them as configuration edited in place causes silent regressions.
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AI & Data
Data quality: the constraint on most AI projects
Model choice rarely limits an AI project. Data availability, labelling and consistency almost always do.
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AI & Data
Managing the cost of AI systems in production
Inference costs scale with usage in ways that surprise teams. A few architectural decisions control most of the bill.
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AI & Data
Computer vision in industrial settings: the conditions decide the outcome
Vision projects succeed or fail on lighting, positioning and consistency far more often than on model architecture.
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