User Story Generation

Challenges User Story Generation addresses

Done by hand, sales engineering means gathering discovery notes or transcript and story settings, working through 2 separate passes over the same material, then producing user stories, acceptance criteria — highest priority story and backlog note. None of it is difficult and all of it is exacting, which is the combination people are worst at holding. The errors that matter are the ones a tired reader does not notice, and they surface later — in a reconciliation, or in somebody’s reply. It waits until someone remembers it, which is usually the point at which it has become urgent. As volume grows the work does not get harder, only longer, and the first thing to go is the checking.

User Story Generation runs that same sequence end to end and returns the result as structured artefacts. What it cannot settle it hands over rather than guesses at, and your correction is kept: it asks “Would a delivery team accept these stories?” after every run, and those answers become the set it is measured against. Nothing that moves money, alters a contract or reaches a customer executes without human approval, and every action is written to an audit log. The gain is in the volume that no longer has to be read, not in removing the judgement.

How it works

Step 1: Writing the stories

First of 2. It works from discovery notes or transcript and story settings and feeds the step after it.

Key Tasks:

• Writing from the run, not from a template: The text is built from what this run actually found, so two sales engineering outputs differ where the underlying records differ.
• Leading with what needs a decision: The exceptions come first and the routine detail follows, because the reader is deciding rather than reading.
• Staying inside the evidence: Nothing appears in the text that is not supported by a record the run examined. Gaps are stated as gaps.

Outcome:

• Artefact ready: A finished artefact, traceable line by line to the records behind it, ready for a person to accept or correct.

Step 2: Writing the backlog note

Last of 2. It takes what step 1 produced and produces user stories and acceptance criteria — highest priority story.

Key Tasks:

• Producing the backlog note: What this step assembles is the backlog note, in the form the reader actually uses it in rather than as a general summary of the run.
• Keeping it traceable: Each claim stays linked to the record behind it, so a reviewer can check a line instead of accepting the whole.
• Writing from the run, not from a template: The text is built from what this run actually found, so two sales engineering outputs differ where the underlying records differ.

Outcome:

• The backlog note assembled: A finished artefact, traceable line by line to the records behind it, ready for a person to accept or correct.

Step 3: Your review, and what it changes

The run ends with a person, not with a result being filed.

Key Tasks:

• Asking a specific question: It asks “Would a delivery team accept these stories?” rather than for a rating. A question about this run is answerable; a score out of five is not.
• Keeping the correction: What you change is recorded against the case that produced it, so the disagreement is retrievable rather than absorbed.
• Building the evaluation set: Those cases become what the agent is measured on. It is scored against your judgement rather than against a general benchmark.

Outcome:

• A measured agent, not an assumed one: The cases User Story Generation handles well and the cases it does not are both visible, and the second list is the one that decides what changes. Nothing is retrained silently on the back of a single correction.

Why use User Story Generation?

• Checks are evidenced, not asserted: Each check records what was expected and what was found. A failure can be understood — and argued with — without re-running anything.
• A batch is one run, not a hundred: It works the whole set in a single pass and returns a row per item with its verdict, so the volume that needs no attention never has to be opened.
• Corrected by the people using it: After each run it asks “Would a delivery team accept these stories?”. Those answers become the evaluation set, which means it is measured against your judgement rather than ours.
• Reads and reports, does not act: It returns a result for review rather than writing changes back on its own. Anything that moves money, alters a contract or reaches a customer needs human approval first.
• Structured results, not prose: All 4 artefacts are structured — user stories, acceptance criteria — highest priority story and backlog note — so a result can be scanned, sorted and acted on instead of read end to end.