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Click on a strategy on the left to see specific implementation details, pros, and cons.
Full Trade-off Comparison
All five strategies scored 0–100 on each dimension. The selected strategy above is highlighted.
| Strategy | Cost | Control | Complexity | Performance | Privacy |
|---|
Market Maturity Estimate
1. Foundation Models
Passive generation based on prompts.
2. RAG & Embedding
Connecting models to live, private data.
3. Agentic AI
Reasoning loops, tool use, and autonomy.
| Stage | Complexity | Autonomy |
|---|
Select a Stage
Click on the timeline steps to understand how AI capabilities are expanding.
Key Challenges (Hover to Mitigate)
Mitigation Strategy
Hover over a challenge
Discover how to solve these problems.
Impact of Mitigation
Scope the Task, Not the Technology
Start by writing down the task exactly as a human does it today, then find the actual bottleneck : is it finding information (retrieval), producing text (generation), or making a judgment call (decision-making)? Most first GenAI features fail because they were scoped as "add AI to X" instead of "solve this one specific bottleneck in X."
| Phase | What Ships |
|---|---|
| Crawl | AI drafts, a human approves every output before it's used. |
| Walk | AI acts directly on high-confidence cases; low-confidence cases route to a human. |
| Run | AI acts autonomously with sampling-based monitoring, not per-case review. |
What a GenAI PRD Needs That a Normal PRD Doesn't
Functional requirements and acceptance criteria aren't enough when the output isn't deterministic. Add these sections:
- Evaluation set & success threshold : a fixed set of real, representative inputs with graded expected outputs, built before the first prompt is written.
- Confidence & fallback behavior : what happens when the model isn't sure. Silent failure is worse than a visible "I don't know."
- Human-in-the-loop points : exactly where and how often a person reviews output.
- Data handling statement : what data leaves your infrastructure, if any, and where it's logged.
- Model-change plan : what happens to this feature when the underlying model is upgraded.
Metrics That Actually Matter
"Accuracy" on an offline test set is the metric engineering will hand you by default. It's rarely the metric that predicts whether the feature is actually working.
| Metric | Why it beats raw accuracy |
|---|---|
| Task success rate | Measures whether the user's actual goal was met, not just whether the output looked plausible. |
| Escalation / override rate | How often a human had to step in : a direct signal of where trust breaks down. |
| Cost per resolved task | Cost per API call hides retries, escalations, and rework. Cost per outcome doesn't. |
| Time-to-resolution | Compared against the process being replaced, not against zero. |
| Adoption / trust rate | Users quietly routing around a feature is a stronger signal than any offline score. |
Buy vs. Build: A PM-Level Checklist
The strategic version of this question belongs to your CTO. Here's the version that determines what you can actually commit to on a roadmap:
- Is this capability core to our differentiation, or table stakes our users expect everywhere?
- How often will requirements change? Fast-moving needs favor an API or RAG; stable needs can justify fine-tuning.
- What's our real tolerance for depending on one vendor's roadmap and pricing?
- Do we have : or can we get : the ML/data capacity to maintain a self-hosted or fine-tuned model, not just launch it?
Common Pitfalls
- Scoping the first release as "do everything" instead of one narrow, well-defined task.
- Treating the model like deterministic software: ship once, never re-evaluate.
- No plan for what happens when the underlying model is upgraded and behavior quietly shifts.
- Underestimating the human review capacity a human-in-the-loop design actually requires at scale.
- Measuring launch success by "it shipped" instead of by task outcomes.
Talking to Stakeholders: A Translation Guide
| What engineering says | What it actually means |
|---|---|
| "Hallucination rate is 4%" | Not "96% correct" : the model states something false with confidence 4% of the time on your specific eval set. Ask what's in that set. |
| "We fine-tuned the model" | It learned style and format, not new facts. It still needs grounding (like RAG) for factual accuracy. |
| "It's RAG-grounded" | Reduces but doesn't eliminate hallucination : retrieval quality is now the bottleneck, not the model. |
A Practical Rollout Checklist
- Define the task narrowly enough to state in one sentence.
- Build an evaluation set from real examples before writing a single prompt.
- Define a fallback for every low-confidence case, not just the happy path.
- Decide who reviews outputs, and how often, before launch : not after an incident.
- Set a cost ceiling per resolved task, not per API call.
- Plan for model version changes before the vendor forces the issue.
- Log inputs and outputs for audit, not just for debugging.
- Define "success" as a number everyone agrees on, not a feeling in a demo.
- Run a shadow or limited pilot period before full rollout.
- Assign an owner for ongoing evaluation : launch is the start, not the finish.
Where Dataknobs fits
Dataknobs' Kreate helps teams build the eval set and pipeline this playbook calls for, AbExperiment tests implementation choices and prompt/model changes against that eval set before they ship, and Kontrols keeps the audit log and human-in-the-loop review trail your PRD needs.
Talk to Dataknobs →