Disclosure: I work on CoHear, an AI recording and note-taking app for iPhone and Android. This is our product perspective, not an independent review. This article was drafted with AI assistance and checked against our product materials.
A transcript is a useful intermediate format. It preserves the conversation, but it does not automatically tell a team what to do next. For anyone building an AI workflow around meetings or interviews, the interesting design question is what happens after speech becomes text.
Here is a checklist for evaluating that step. It also explains the workflow CoHear is designed around, without claiming that generated notes replace human review.
1. Start with a small output contract
“Summarize this meeting” leaves the system a lot of room to guess. A more useful output has a clear shape:
- Decisions: what was agreed, and what is still unresolved.
- Actions: the next step and its owner, only when the conversation identifies one.
- Questions: items that need confirmation rather than a confident-sounding answer.
- Evidence: enough context to check a consequential statement against the recording.
The important distinction is between extraction and inference. If no owner was assigned, an empty owner is better than a plausible name. If a date was only a suggestion, the summary should not quietly turn it into a commitment.
2. Keep speaker context visible
A sentence such as “we can ship Friday” means different things when it comes from an engineer, a customer or a facilitator repeating a question. Speaker labels are useful, but they can be wrong. Treat them as a navigation aid that needs checking before a quote or commitment is attributed to someone.
CoHear supports speaker-labeled transcripts and speaker filtering. The practical test is simple: can the reviewer find the relevant speaker and understand the surrounding exchange without rereading the whole transcript?
3. Choose the artifact for the next reader
The same recording can lead to different jobs:
- A project owner needs decisions and actions.
- A stakeholder needs a short summary or presentation.
- A researcher needs themes and quotes with context.
- A student needs a study plan and practice questions.
These are different outputs, not just different summary lengths. A useful slide should be understandable without the recording. A practice question should be checked against the source before it becomes revision material.
CoHear can turn a recording or imported audio into transcripts, summaries, action items, slides, infographics and study materials. The right choice depends on what the reader needs to do next.
4. Make review part of the workflow
Before sharing any generated result, check the details that would be costly to get wrong: names, amounts, decisions, owners and dates. Distinguish a draft from an approved record. A polished layout does not make an uncertain statement more reliable.
A useful acceptance check is to ask a person who attended the meeting to read only the output. Can they identify the decisions, see the unresolved questions and understand the next steps? Then compare those details with the source.
5. Test the complete path
Evaluating only transcription misses the actual user task. Try a short, low-risk recording with permission from the participants. Generate one output, correct any errors, and share it with the intended reader. Measure the work needed to reach a usable result, rather than only the amount of text produced.
The workflow is: recording → transcript → useful draft → human review → shared result. The review step stays in the loop.
If you want to try that workflow on a phone, CoHear is available for iPhone and Android with a free plan. Start with one recording you have permission to use and one concrete output you need.
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