Retrieval determines whether GenAI has the right context before it answers. This accelerator assesses sources, chunking, embeddings, ranking, freshness, permissions, and observability so outputs are grounded in usable evidence.
Many Teams Retrieve More Context, Not Better Context
Many GenAI issues are retrieval issues hiding as model issues. When context is stale, incomplete, or poorly ranked, responses sound confident but lose grounding and trust.
- Is our retrieval approach grounding GenAI in the right sources?
- Where could weak retrieval undermine answer quality?
- Do we have the retrieval discipline to improve grounding, trust, and performance?
Improve Retrieval Without Adding Noise, Drag, or Risk
We help leaders pinpoint the retrieval gaps that matter most so GenAI pulls more relevant, timely, and usable context that strengthens grounding, answer quality, and trust at scale.
- Identify key stakeholders
- Explore what “good” looks like
- Explore Real-World Use Cases
- Review Key Competencies
- Assess Your Readiness
- Add Comments for Context
- Define Group Readiness
- Identify Mis-Alignment
- Capture Group Themes
Plan
- Understand High-Impact Gaps
- Explore Gap Closure Options
- Prioritize For Impact & Effort
- Define Key Steps
- Align on Ownership
- Define Target Timeline
- Committed Target
- Stretch Goals
- Controls
- Execute your plan
- Mitigate Risks
- Validate Your Impact
- Identify Stakeholders
- Communicate Changes
- Action Feedback
- Re-baseline Readiness
- Select Next Gaps
- Update your readiness plan
Outcomes you can expect
See which retrieval gaps most weaken grounding, relevance, trust, and scale.
Align around the retrieval priorities that matter most for better grounded GenAI.
Prioritize the improvements that most strengthen context quality, freshness, and fit.
Build a stronger retrieval foundation for more relevant and trustworthy GenAI.
Improve the odds that GenAI uses the right context to produce better answers.
Frequently Asked Questions
- Who is this GenAI Retrieval readiness accelerator for?
Product, AI, data, platform, and knowledge teams grounding GenAI responses in source content. - When should we assess our GenAI Retrieval readiness?
Assess before retrieval quality limits grounding, relevance, trust, or freshness. - How is this different from a standard RAG implementation review?
It assesses retrieval readiness across sources, permissions, chunking, ranking, freshness, and observability.
- What exactly gets assessed in GenAI Retrieval readiness?
We review sources, chunking, embeddings, retrieval logic, ranking, freshness, and observability. - What inputs and artifacts should we bring into the accelerator?
Bring source inventories, RAG diagrams, chunking rules, retrieval logs, and quality examples. - What will we receive at the end of the accelerator?
You get a retrieval-readiness view, priority gaps, and a grounding-improvement plan.
- How long does the accelerator take?
Plan on roughly 12 weeks, from diagnosis through prioritized gap closure. - How do the three phases work in practice?
Diagnose retrieval gaps, align priorities, then close the issues that most affect grounding. - How hands-on is the 12-week period?
Hands-on enough to pressure-test sources, retrieval settings, evaluation data, and monitoring.
- Which teams should participate?
Include product, AI, data, platform, knowledge, security, and analytics owners. - How much time should leaders and working teams expect to commit?
Sponsors join key decisions; working teams support diagnostics, reviews, and action planning. - How will the right teams work together during the accelerator?
Teams align on trusted sources, retrieval design, permissions, and quality signals.
- What changes when GenAI Retrieval readiness improves?
Responses become better grounded, more relevant, and easier to trust. - How quickly can we act on the findings?
Immediately. The accelerator prioritizes gaps leaders can act on right away. - What should we do after the readiness assessment is complete?
Prioritize source quality, retrieval logic, ranking, freshness, and observability fixes.