Grounded AI Delivery Sprint (10-14 days)
A productized sprint that equips agencies to build source-cited, retrieval-augmented AI features for clients, cutting document ingestion and semantic search plumbing time while keeping vendor options open. Time: 10-14 days.
By InnovaAI ResearchPublished
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Grounded AI Delivery Sprint (10-14 days)
A productized sprint that equips agencies to build source-cited, retrieval-augmented AI features for clients, cutting document ingestion and semantic search plumbing time while keeping vendor options open.
- Client has a defined set of documents or knowledge sources to ground AI outputs
- Access to a vector database or managed retrieval API (e.g., a context engine like Ragie)
- An LLM API key with sufficient quota for development and testing
- A staging environment with version control and CI/CD
- A named client stakeholder to approve evaluation criteria and sign-off
- 1.Audit client's existing AI usage and document sources
- 2.Define use cases for grounded answers (e.g., support, research, content)
- 3.Identify evaluation metrics: citation accuracy, retrieval precision, latency
- 1.Select retrieval stack: managed API vs. self-hosted vector DB
- 2.Map document ingestion pipeline: connectors, parsing, chunking
- 3.Set up project repository and CI/CD skeleton
- 1.Ingest a sample corpus (e.g., 100-500 documents)
- 2.Configure chunking strategy: size, overlap, metadata
- 3.Test retrieval on 20-30 representative queries
- 1.Build evaluation harness: golden set of Q&A pairs
- 2.Measure baseline retrieval precision and recall
- 3.Document initial failure modes (e.g., missed context, wrong chunk)
- 1.Tune retrieval: hybrid search, reranking, filters
- 2.Iterate on chunking and metadata to improve precision
- 3.Record performance metrics in a shared dashboard
- 1.Design prompt template that enforces citation format
- 2.Implement guardrails: source verification, hallucination checks
- 3.Integrate retrieval with LLM call (RAG pipeline)
- 1.Build a simple UI or API endpoint for testing
- 2.Run end-to-end tests on 50+ queries
- 3.Collect qualitative feedback from internal team
- 1.Optimize for latency and cost: caching, batching, model tier
- 2.Implement fallback for low-confidence retrievals
- 3.Stress test with concurrent requests
- 1.Prepare client demo: show before/after retrieval quality
- 2.Document configuration and operational runbook
- 3.Plan for scaling: additional sources, connectors
- 1.Deploy to staging with client's data
- 2.Run UAT with client stakeholders
- 3.Collect sign-off on evaluation criteria
- 1.Address UAT feedback: refine prompts, retrieval, UI
- 2.Finalize documentation and handover materials
- 3.Set up monitoring and alerting for production
- 1.Deploy to production
- 2.Conduct training session for client team
- 3.Deliver final report with metrics and recommendations
Agencies can charge a premium for grounded AI because clients face high risk from hallucinated outputs, as seen in recent incidents where fabricated sources appeared in major consulting reports. By abstracting retrieval behind an evaluation layer, agencies avoid vendor lock-in and can swap providers as benchmarks evolve, protecting margin and delivery quality.
- Deployed RAG pipeline with source-cited responses
- Evaluation report with precision/recall metrics and sample queries
- Operational runbook covering ingestion, tuning, and monitoring
- Client training session and recorded walkthrough
- Maintenance retainer proposal with SLA
The client's AI feature returns source-cited answers with at least 90% citation accuracy on the agreed golden set, and the client has signed off on the evaluation report.