Tutorial ID | T-22 |
Section | AI & Analysis |
Title | Generating parts of the report using Al |
Subtitle | Draft report sections with AI in small, verifiable pieces without losing accuracy or your own judgment. |
Before You Begin
You have a case with documents uploaded, and checked for duplicates and missed files.
You have access to saved AI PromptHub prompts, or you're ready to write a scoped prompt for the section you want to generate.
What You Will Accomplish
By the end of this tutorial, you will know how to generate report sections in pieces the AI can draft accurately and you can verify quickly. You will be able to keep the AI on facts and structure, and keep judgment calls like causation and conclusions for yourself.
Why Does this Tutorial Matter
A narrow, well-defined request worked from a complete, correctly organized document set produces output that stays close to the record and is straightforward to verify. Used this way, AI generation removes the slow, mechanical part of building a report, so your time goes to the analysis and judgment only an expert can provide.
Asked to write everything at once, the AI produces text that's harder to check and more likely to drift from the sources — adding detail the records don't contain, smoothing over gaps, or drifting into conclusions that are yours to draw, not the model's. Whatever the AI generates is a draft section, not a finished one, until it passes through the Validation Analysis and your own review.
Steps
Confirm all relevant documents are uploaded and assigned, and run the duplicate and missed-file checks before generating anything.
Generate in narrow, well-defined pieces — one episode, one provider, or one date range at a time — rather than the whole report at once.
Work from saved AI PromptHub prompts so terminology and structure stay consistent across sections.
Tell the AI what to leave out — for example, list the documented findings and instruct it not to infer causation.
Compare the generated chronology against the dates in the source documents to catch sequencing errors and duplicated events.
Run every generated section through the Validation Analysis and your own source check before adding it to the report.
Keep conclusions, causation, and opinion as your own work — let the AI assemble the facts, and add the analysis yourself.
Never export a generated section straight from generation without validating it first.
Tip Treat generation and verification as one motion, not two stages. Generate a piece small enough that you can validate it before moving to the next — it's faster overall than generating everything and untangling the errors afterward. |
What to Do if Something Goes Wrong
The list below covers the most common problems teams run into when generating report sections with AI. Each entry follows the same pattern: what you'll notice, why it likely happened, a real example, and how to fix it.
Problem: A generated section includes detail that isn't in any of the source documents.
Likely cause: The AI was asked to summarize broadly, and it added plausible-sounding detail to fill in a gap rather than leaving the gap as it was.
Example: Asked to summarize a treatment episode, the AI generates a specific diagnosis date that appears nowhere in the underlying records.
Fix:
Constrain the prompt to the source — instruct the AI to use only the provided documents and to leave a gap rather than invent detail.
Generate in smaller units, such as a single episode of care, rather than a whole report at once.
Validate each generated statement and remove anything that can't be traced to a document.
Problem: A generated section states causation, liability, apportionment, or prognosis as fact.
Likely cause: The AI was asked to draft broadly rather than being constrained to the factual chronology, so it produced a conclusion that is a professional judgment call, not a documented fact.
Example: A generated summary states that a fall caused a specific injury, when the records only document the fall and the injury separately without linking them.
Fix:
Keep AI generation to the factual chronology — what happened, when, and recorded by whom.
Reserve interpretive and conclusory language for your own drafting.
Where the AI has editorialized, rephrase it to neutral, descriptive language tied to the records.
Problem: The same provider, injury, or procedure is referred to differently across sections.
Likely cause: Sections were generated separately without an agreed standard for key terms, so each one used slightly different language.
Example: One section refers to a provider as “Dr. Smith” and another as “the treating orthopedist,” making the report look inconsistent.
Fix:
Decide on standard forms for key names and terms before or during generation.
After generating, use Search to check that each term is used consistently throughout.
Standardize variants and align them with how the source records describe them.
Problem: A generated section reads smoothly but doesn't flag a gap in treatment or a conflict between records.
Likely cause: A fluent summary can paper over missing records or conflicting accounts instead of surfacing them, especially when the AI is asked to produce polished prose.
Example: A chronology skips silently over a six-month treatment gap, and the smooth narrative gives no indication that anything is missing.
Fix:
After generating, compare the narrative against the document set for gaps in treatment, missing dates, or conflicting histories.
Make gaps and conflicts explicit in the report rather than letting the AI smooth them away.
Use AI Chat to ask what is missing or what disagrees, then verify the answer against the records.
Problem: A generated section contains weak or incorrect content that traces back to a poor source document.
Likely cause: The document the AI drew from was an illegible scan, a duplicate, or a mislabeled file, and the draft inherited that problem.
Example: A poorly scanned page is misread, and the generated section repeats the misread detail as if it were accurate.
Fix:
Confirm the documents you're generating from are complete, legible, and deduplicated.
Where a source is poor, flag the affected statements for closer review.
Regenerate from corrected or supplemented documents where necessary.
Problem: A generated section doesn't match the register the report needs, such as a neutral chronology or an advocacy summary.
Likely cause: The prompt didn't specify the audience or required tone, so the AI defaulted to a register that doesn't fit the purpose.
Example: A section meant to read as a neutral chronology comes back sounding argumentative, which doesn't fit its place in the report.
Fix:
Tell the AI the audience and the required tone in the prompt.
Review the output for register — neutral and factual for a chronology, appropriately framed for an advocacy document.
Adjust the wording yourself where the tone doesn't fit the purpose.
Summary
Every generated section is now built from a scoped prompt, checked against the source, and validated before being added to the report — so the AI handles the facts and structure, and you keep the judgment calls.