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.
ZooBieDoo Infotech Engineering TeamRead
Key takeaways
- Establish naive baselines and publish the comparison.
- Method should follow the data, not fashion.
- Prediction intervals change decisions; point forecasts mislead.
- Capture overrides with reasons and compare outcomes openly.
Start with a baseline you must beat
Before any modelling, establish naive baselines: last period's value, the same period last year, a moving average. A meaningful share of forecasting projects produce models that do not beat these convincingly.
Publishing the baseline comparison keeps the project honest and gives an immediate answer to whether the sophistication is earning anything.
Match the method to the data
- Classical methods for stable series with clear seasonality — interpretable and hard to beat.
- Gradient boosting when external drivers such as promotions or weather matter.
- Hierarchical reconciliation when forecasts must agree across store, region and group.
- Similarity-based approaches for new items with no history.
Communicate uncertainty
A point forecast implies precision the model does not have. Prediction intervals let a planner distinguish a confident forecast from a guess, and that distinction changes decisions.
Where the model has thin signal — new products, promotional periods, unusual events — say so explicitly and ask for human judgement rather than producing a number that looks like the others.
Design for override
Planners will override forecasts, and sometimes they should. Capture every override with a structured reason and compare outcomes.
Reporting where the model beat the planner and where the planner beat the model, openly, is what builds the trust that gets forecasts used at all.
Making forecasts usable in planning
A forecast only creates value when it reaches a decision. That usually means integrating it into the tool where planning actually happens rather than delivering it as a separate report.
Present at the granularity decisions are made at — if buying is weekly by category, a daily item-level forecast forces the planner to aggregate it themselves, and many will not.
Provide the drivers alongside the number. A planner who can see why the forecast moved is far more likely to act on it than one presented with an unexplained figure.
Frequently asked questions
Enough to cover several cycles of the seasonality you care about typically two to three years for annual patterns. Less history means wider intervals, which should be stated rather than hidden.
Similarity-based approaches using comparable items, combined with explicit human input. Any model will have thin signal here and should say so.
It depends entirely on the volatility of the series. The useful question is whether the forecast beats the baseline by enough to change decisions profitably.
