If you’re building an AI-powered product, it’s easy to focus on MVP and model choice. But what separates short-term launches from long-term success is data – specifically, the data you collect from real usage.
Future improvements – fine-tuning, better accuracy, personalization, cost optimization – all depend on what you’re capturing now. This checklist is designed to help product leaders, engineers, and AI teams plan ahead.
Whether you’re fine-tuning, switching models, or planning long-term optimization, teams like S-PRO can help bridge product strategy with AI development best practices.
1. User Prompts and Inputs
This refers to the raw queries or messages users send to your AI system. These inputs reveal how people interact with your product in real-world settings. Logging them helps train better intent recognition, improve prompt structures, and surface common questions or behaviors.
Log raw prompts or inputs submitted by users are invaluable for:
- Training future versions of your model
- Understanding intent patterns
- Prompt tuning and evaluation
Make sure to store them securely and anonymize when needed.
2. User Feedback on AI Output
Feedback can come in the form of star ratings, binary thumbs up/down, or written comments. It tells you whether the AI’s output actually met user expectations. Over time, this feedback becomes labeled data that can improve model responses through fine-tuning or ranking algorithms.
- Highlight where your model performs well or poorly
- Feed reinforcement learning pipelines
- Prioritize use cases for improvement
Over time, this gives you labeled training data directly from real users.
3. Edits and Corrections Made by Users
When users tweak AI-generated text – rewrite a sentence, fix a summary, edit a suggestion – capture the before/after versions.
This supervised feedback is critical for fine-tuning. You’re learning how users really want the model to behave.
4. Edge Cases and Failures
Track what doesn’t work:
- Requests that trigger fallback flows
- Model errors or timeouts
- Irrelevant or hallucinated answers
Flag these automatically and tag them for review. They’ll form the basis of your regression testing and failure-mode tuning later.
5. Metadata for Every Interaction
Metadata provides operational context. It includes things like which model was used, how many tokens were processed, how long it took to respond, and what device the user was on.
- Model used (GPT-4, Mistral, Claude, etc.)
- Input/output token count
- Response time and latency
- Device, browser, or platform type
- Time of day, session length
This data helps debug latency, cost, or context window issues. You’ll also use it to benchmark performance when experimenting with different models.
6. Search + Abandonment Signals
This tracks user behavior around incomplete or unsatisfying experiences. For example, if a user initiates a query but abandons it or takes no action afterward. These signals help identify weak spots in your content or model logic.
Did the user:
- Start a query but bounce?
- Read a response and take no action?
- Use fallback search (e.g., Google it manually)?
Log these actions to understand where expectations are being missed – and which features need better alignment with user intent.
7. Consent-Based Ground Truth Data
With explicit user consent, you can collect original content – like documents, emails, or support tickets – that the AI is supposed to summarize, tag, or respond to. These examples form your “ground truth” for fine-tuning models on domain-specific tasks or industry verticals.
With proper permissions, collect:
- Uploaded documents
- Source files for summarization or Q&A
- Manually written user content
This “ground truth” is crucial when fine-tuning models for domain-specific tasks like legal, finance, or healthcare.
Generative AI development companies often use this data to build models that outperform generic APIs on narrow use cases.
8. Session-Level Interaction Patterns
Looking beyond isolated prompts, session data captures the flow of user behavior across multiple steps. Did they continue using the product? Did they follow up with another question? This helps assess whether AI features are actually contributing to task completion or decision-making.
Think beyond the prompt. Capture the user journey:
- What did they do before and after an AI response?
- Did they ask follow-ups?
- Did they drop off or complete a task?
These patterns help you evaluate the real-world impact of your AI system, not just single-turn quality.
9. Cost and Token Usage Metrics
Tracking token usage at a granular level (by feature, user type, or endpoint) lets you spot expensive interactions and inefficient prompt designs. It also informs your pricing model and whether you should route different tasks to lighter models.
Start tracking token spend per:
- Feature
- User
- Model type
This helps you:
- Detect inefficient prompts
- Identify low-value vs. high-impact features
- Plan hybrid strategies or model routing (e.g., GPT-4 vs. Mistral)
10. Segment-Level Performance
Segmenting by role, user type, or industry helps identify variation in model performance across your audience. This enables you to personalize responses, build different prompt strategies, or decide where fine-tuning will have the highest impact.
If your product serves different user groups (e.g., legal teams vs. marketers), tag sessions by segment. This lets you analyze:
- Which group gets more value from AI?
- Where does performance lag?
- Is personalization required for specific segments?
You may discover that one audience drives most feedback or requires a tailored prompt strategy.