omni-ai-optimizer

Tuning AI context improves Blobby agent accuracy by configuring ai_context to map business terms directly to model fields, such as linking revenue to order_items.total_revenue, ensuring the agent retrieves precise and relevant data for queries.

Reducing noise in AI prompts involves ai_fields curation to keep prompts focused only on relevant data fields, filtering out unnecessary information that could confuse the model and degrade response quality.

You create AI-specific topic variants for targeted analysis by applying AI topic extensions, which allow you to configure specialized contexts and sample queries that guide the agent's behavior for distinct analytical subjects.

Your AI agent returns incorrect query results because business terms are not properly mapped to model fields. Configuring ai_context and adding sample_queries teaches the agent the correct field associations through concrete examples.

Yes, you need YAML frontmatter providing name and description to configure sample queries. This documentation ensures safe usage and helps the system understand the query examples you add to teach the Blobby agent.

Limitations when mapping business terms to model fields include handling edge cases where terms are ambiguous or overlap. Troubleshooting guidance helps tune context and improve answer quality by addressing these specific mapping conflicts.