Regression Testing Search API Integrations in CI
Deterministic testing assumes search APIs behave the same way every time, but they don't.
Daniel Ellison
Contributing Editor
Daniel Ellison
Daniel Ellison covers api benchmarks, deep research and rag infrastructure for Search Intelligen.
12 stories
Domain-Specific Benchmark Leaderboards for AI Search
Domain-specific benchmarks measure what actually matters in production search systems.
Daniel Ellison
Open Benchmarks vs Proprietary Benchmarks for Search Infrastructure
Open benchmarks let you verify claims; proprietary ones only let vendors control the narrative.
Daniel Ellison
Building a Custom Eval Suite for Search-Augmented LLMs
Separate retrieval scoring from generation scoring to catch citation gaps most evals miss.
Daniel Ellison
Building an Internal Search API Eval Suite
Test each failure mode independently so regressions don't hide behind a single misleading score.
Daniel Ellison
Evaluating Search API Freshness for AI Agents
Stale data in agents' context windows causes failures that retraining won't fix.
Daniel Ellison
How to Benchmark Search APIs for AI Applications
Evaluate search APIs on agent behavior, not human browsing patterns.
Daniel Ellison
Handling Contradictory Web Sources in Agent Pipelines
Build conflict resolution into your pipeline before the model sees contradictory sources.
Daniel Ellison
Agentic Retrieval vs Traditional Search for Enterprise Research
Agents decompose complex questions and validate answers.
Daniel Ellison
Synthesizing Multi-Source Research Outputs in LLMs
Iterative retrieval and task-specific routing beat single-pass search on complex questions.
Daniel Ellison
Semantic Search vs Keyword Search in AI Applications
Keyword search wins on identifiers; semantic search wins on intent—hybrid approaches handle both.
Daniel Ellison
RAG vs LLM-Only Responses
RAG cuts hallucinations by up to 71% but still fails in specialized domains.
Daniel Ellison