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How to distinguish letters of intent from committed demand for AI code review tools
AI code review tools: A letter of intent can communicate interest, but its meaning depends on what it actually says and what actions followed.
A letter of intent can communicate interest, but its meaning depends on what it actually says and what actions followed. Distinguish expressions of interest from authorized, funded, and enforceable commitments. Do not infer legal effect from the document's title.
Start with the decision
Read the relevant document with qualified counsel where needed. Separately record budget status, approvals, implementation work, and the next decision. Ask what evidence would exist if the buyer were preparing for a real purchase.
The illustrative scenario
This is an educational workbook. The company or fund and all numerical inputs below are fictional. They are not ADIN results, customer claims, market benchmarks, or a forecast.
The fictional service suggests review comments on pull requests for an engineering team. Maintainers remain responsible for accepting a finding and deciding whether a change is ready. Review traffic varies with release cycles.
Too many plausible but unhelpful comments can increase reviewer work. A severity label has little meaning unless the team agrees which findings require action.
Turn the scenario into an evidence decision
Consider a review of AI code review tools in which a confident explanation arrives before the supporting records. Start with the first question in the table. Write down what an answer would change: the next interview, the experiment's scope, a request for specialist review, or a decision to wait. A question without a consequence can produce a longer document while adding little understanding.
Next, inspect the record named in the table and record its date, owner, permission boundary, and limitations. Keep a labeled sample of true defects, false alarms, dismissed suggestions, and defects missed by both workflows. Record repository and change type so aggregate numbers remain interpretable. If the record contradicts the explanation, preserve both accounts and ask which definition, period, or workflow differs. Do not silently select the version that makes the opportunity look strongest.
Finish with a conditional statement: if the specified evidence supports the claim under these conditions, take the named next step; otherwise investigate the unresolved question. A useful traction narrative describes the exact commitment and its limits. If nothing has changed in the buyer's behavior, the document may still inform learning while offering little evidence about timing or future revenue. This worked exercise is about the decision process. It does not assert that a fictional company has met a real diligence standard or that a template can authorize an investment.
| Item | Illustration |
|---|---|
| Decision question | What does the document state? |
| Case-specific constraint | Too many plausible but unhelpful comments can increase reviewer work. A severity label has little meaning unless the team agrees which findings require action. |
| Record to inspect | Keep a labeled sample of true defects, false alarms, dismissed suggestions, and defects missed by both workflows. Record repository and change type so aggregate numbers remain interpretable. |
| Comparison to preserve | Compare actionable findings and reviewer effort on similar changes. A tool that produces more comments is not automatically finding more consequential problems. |
| Unresolved case question | What makes a finding actionable? |
Use this worksheet
Answer the questions with a source, date, owner, and next action. Mark an unknown as unknown. Use public or permissioned information and involve a qualified specialist when a question requires expertise beyond the team’s competence.
- What does the document state?
- Who authorized it?
- Is a budget approved?
- What work has begun?
- Which conditions remain unresolved?
Questions specific to this scenario
- What makes a finding actionable?
- Which changes are excluded?
- Who adjudicates reviewer disagreement?
Keep a labeled sample of true defects, false alarms, dismissed suggestions, and defects missed by both workflows. Record repository and change type so aggregate numbers remain interpretable.
A concrete next test
Ask two maintainers to label the same permissioned set independently before looking at the tool's findings. Investigate disagreements about severity and actionability. A shared rubric gives the next comparison more meaning than counting comments that nobody would act on.
Interpret the result
A useful traction narrative describes the exact commitment and its limits. If nothing has changed in the buyer's behavior, the document may still inform learning while offering little evidence about timing or future revenue.
Compare actionable findings and reviewer effort on similar changes. A tool that produces more comments is not automatically finding more consequential problems.
Sources and method
These primary resources provide background for the concepts. The fictional scenario, calculations, and worksheet are ADIN educational examples; the sources do not report or validate those invented results. Reviewed October 11, 2026.