Observed Signal · Apr 14, 2026 · Technical Release · Source: DEV Community · Impact: 1/5 · Sentiment: Neutral
Local eval loop added to personal AI assistant
A developer added a local evaluation loop to their self-hosted AI assistant that scores every interaction using a local Ollama model on accuracy, relevance, and appropriate confidence. Interactions below a threshold trigger a generated reflection that diagnoses what went wrong; those reflections are batched into DSPy to periodically optimize system prompts. After roughly 800 scored interactions the author observed repeatable patterns: the assistant was frequently overconfident on estimates (timelines, complexity, quantities) and biased toward underestimating, while shorter, more direct answers tended to score better. The author cautions the Ollama scoring model is imperfect and that DSPy converges slowly on single-user datasets. The project and code are published on GitHub (sliamh11/Deus).
Small-scale developer experiment about local evaluation and prompt optimization for a single-user AI assistant; interesting technically but not industry-shifting or broadly generalizable.
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Key Takeaways & Evidence Grounding
- Every assistant interaction is scored locally by an Ollama model on accuracy, relevance, and appropriate confidence.
- Interactions scoring below a threshold trigger a reflection prompt that generates a short analysis of failures.
- Generated reflections feed into DSPy, which is used periodically to optimize underlying system prompts.
- After ~800 scored interactions, the assistant showed systematic overconfidence on estimates and a bias toward underestimating.
- Shorter, more direct answers consistently scored higher than longer, more thorough responses; author notes scoring model and DSPy have limitations on single-user data.
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