The shared vocabulary of the edit bay, defined the way working editors actually use the words.
What is a paper cut in documentary editing?
A paper cut is a written assembly of interview selects in the order the editor plans to use them. It is the blueprint for the rough cut. Here is how it works.
What is a radio edit, and how is it different from a paper cut?
A radio edit is the audio-only assembly that proves your story works before you cut any picture. Here is how documentary editors use it and where it sits next to the paper cut.
What is a beat sheet, and how do documentary editors use one?
A beat sheet is the ordered list of story beats your documentary is going to land. Here is what a beat is, what a beat sheet does, and how it differs from a paper cut.
Assembly cut, rough cut, fine cut: what each one actually means
Documentary edits move through three named milestones. Each one answers a different question and sits at a different runtime. Here is what they are and how to tell which one you are in.
The documentary post-production workflow, end to end
From dailies to final delivery: every stage a documentary passes through in post, what each stage produces, what it costs in time, and where things stall.
Beat sheet vs paper cut: what each one does and when
Beat sheets define structure. Paper cuts handle assembly. They work in sequence, not in competition. Here is how the two documents relate and why skipping the beat sheet creates problems in the paper cut.
Documentary beat sheets: what they contain and when to write them
A documentary beat sheet is not a screenplay beat sheet. Here is what a documentary beat actually says, how many beats a feature needs, and when directors write them before shooting versus after.
Paper cut, radio cut, paper edit: the same workflow, different names
Three terms from different production traditions describe the same practice: assemble interview selects in writing before touching the NLE. Here is where each term comes from and when the distinction actually matters.
Character profiles in documentary: what to include and when to write them
A documentary character profile gives directors a view of who they have across all interview hours. Here is what to include, what to leave out, and when in the production timeline it pays to write one.
FCPXML explained: what it carries, what it does not, and the import gotchas
FCPXML carries edit decisions, clip references, timecodes, roles, and markers. It does not carry media. Here is how Premiere, Resolve, and Avid handle it differently, and where timecode offsets and missing audio come from.
SRT vs VTT: which subtitle format for documentary delivery?
SRT is more widely supported. VTT has styling hooks. Here is what each format contains, which downstream tools expect which, and where mismatches cause dropped cues.
How interviews get from a microphone to a transcript you can cut from.
Transcript-based editing: cutting interviews from the page, not the timeline
Transcript-based editing means selecting interview clips by reading the words instead of scrubbing through audio. Here is why it dominates modern documentary post and how the workflow runs.
How to transcribe an interview for documentary editing
A practical guide to transcribing interviews for documentary post: when AI is good enough, when human review is needed, how to handle speakers, timecode, and overlap.
Transcription accuracy: what WER measures and what it misses
Word error rate is the standard accuracy metric, but it understates the problems that matter most for documentary audio: proper nouns, accents, crosstalk, and technical terms. Here is what WER measures and what it does not.
Live transcription providers compared for interview work
How to choose between Deepgram, Google STT V2, AssemblyAI, and OpenAI realtime for live interview transcription. The key axes are latency, diarization quality, language coverage, and cost per minute.
Google Translate, DeepL, and OpenAI: which engine for documentary interviews?
How Google Translate, DeepL, and OpenAI translation handle tone, names, and idioms differently, and why the engine that works for corporate content is often wrong for documentary speech.
Translating documentary interviews: practical notes for editors
How to handle proper nouns, speaker voice, and over-polish when sending documentary transcripts through machine translation, and when to flag a segment for a human reviewer.
Speaker diarization explained: how it works and where it fails
Diarization assigns speaker labels to audio segments without knowing who the speakers are. Here is how voice prints work, why similar vocal profiles cause problems, and how merge thresholds control the output.
Semantic search explained: why keyword search fails for interview archives
Embedding-based semantic search finds passages by meaning rather than by matching words. Here is what an embedding is in plain terms, how the search works, and where it still misses things.
RAG for documentary archives: how retrieval-augmented generation works
Retrieval-augmented generation answers questions about indexed interview transcripts by retrieving relevant chunks and grounding the model's output in those chunks. Here is how the pipeline works and where it still fails.
Grounded LLM generation: what it means and where it still goes wrong
A grounded language model is constrained to produce output traceable to specific source chunks. Here is what grounding means in practice, why it produces usable output, and where the model can still hallucinate.
Picking the right software for transcription, translation, and post.
Speech-to-text providers compared: Whisper, Deepgram, AssemblyAI, Google STT
An honest comparison of the four speech-to-text providers most documentary editors actually use, scored on accent handling, diarization, latency, and cost per hour of audio.
PaperCuts vs Otter.ai: which fits a documentary interview workflow?
Otter.ai is built for meeting notes. PaperCuts is built for multi-speaker documentary interviews, speaker identity across files, and a post-production assembly layer. The gap is not about accuracy.
PaperCuts vs Trint: where they overlap and where they differ
Both produce searchable multi-speaker transcripts and export in multiple formats. The divergence is in what comes after: Trint has no beat sheet or paper cut assembly; PaperCuts has no NLE-style video scrubbing.
Self-hosted pyannote vs built-in diarization: when each makes sense
Running your own pyannote diarization stack makes sense for custom fine-tuning, offline requirements, or research. For production documentary work, the real cost is not the model but the annotation and tuning loop.
PaperCuts vs Descript: planning versus execution in documentary editing
Descript's text-based timeline editing and a spreadsheet paper cut both start from transcript text, but serve different moments in the workflow. Descript is a nonlinear editing environment; a paper cut is a plan.