What Logging Actually Is
Logging is the work of describing what is in each clip with enough detail that you can find it later. On a small project, you can rely on memory and visual scrubbing. On any project past a couple of hours of footage, you need explicit descriptions because you cannot remember what is in clip 47 of camera B without notes.
The traditional logging workflow has an assistant editor (or the editor themselves on smaller projects) watching every clip, writing a brief description, and adding it to the clip's metadata or to a logging document. The descriptions cover several dimensions:
- Shot type: wide, medium, close-up, over-the-shoulder, etc.
- Subject: who or what is in the frame
- Action: what is happening
- Audio: dialogue, music, ambient sound, silence
- Quality: usable, has issues, unusable
- Notes: editor-specific observations relevant to the project
Done well, logging produces a navigable footage library. Done poorly or skipped, you spend the entire edit lost in your bins.
The volume of logging work scales linearly with footage volume. A 30-minute shoot might produce 30 clips needing 30-90 minutes of logging. A multi-day branded shoot might produce 200 clips needing 8-15 hours of logging. A documentary shoot might produce 800+ clips needing weeks. The labor is repetitive and tedious, which makes it both expensive (assistant editor hours add up) and ripe for automation.
What AI Can Tag Automatically
Modern AI tools can generate most of the descriptive logging metadata automatically using a combination of computer vision, audio analysis, and language model reasoning. The capabilities cover most of what traditional logging produces.
Shot type classification. AI vision models reliably identify whether a shot is wide, medium, close-up, extreme close-up, over-the-shoulder, or other standard framings. Accuracy is typically 90-95% on standard footage. The errors are mostly on ambiguous cases (where multiple shot types could apply) rather than miscategorizations.
Subject identification. AI tags clips with what is in the frame: people (including counts), objects, settings, and basic visual context. Accuracy is high on common subjects (people, vehicles, buildings) and lower on specialized content (specific products, branded signage).
Action classification. AI tags what is happening: talking, walking, demonstrating, working, gesturing. Accuracy is moderate -- common actions are well-classified, while subtle or unusual actions are hit-and-miss.
Audio characteristics. AI identifies whether a clip contains dialogue, music, ambient sound, or silence, with timecodes for each. Accuracy is high. Speaker count and basic vocal characteristics are also tagged.
Quality detection. AI flags clips with technical issues: focus drift, exposure problems, audio noise, motion blur. Accuracy varies by issue type but the flags are useful starting points for human review.
Composition tags. Center-framed vs off-center, static vs moving, stable vs handheld. These are reliable and useful for finding specific visual styles.
Speaker identification (multicam). For multi-person dialogue content, AI identifies who is speaking at every moment with 90%+ accuracy when faces are visible.
Dialogue content (transcript). Every spoken word in every clip is transcribed with timecodes, making content fully searchable.
The most surprising thing the first time you use AI logging is realizing how much of traditional logging was mechanical description work. The AI tags are roughly equivalent to what an entry-level assistant editor would produce -- not what a senior editor would produce, but the senior editor's notes are not really the descriptive part anyway. They're the judgment layer that AI can't do.
What Still Needs Human Judgment
The descriptive layer is largely automatable; the judgment layer is not. Knowing where the boundary lies helps you allocate human time effectively.
Take quality opinions. AI can identify takes with technical problems but cannot reliably assess take quality on subjective dimensions. Which take has the most energy? Which take feels most authentic? Which take has the cleanest emotional arc? These are editor calls.
Project-specific context. AI does not know that the founder's mention of "the early days" at 14:32 is the moment the script will revolve around. AI does not know that the customer's specific phrase about "saving 30 hours a week" is the headline you need. Context that comes from the brief, the script, or the editor's understanding of the project is human-only.
Emotional moments. AI tags audio characteristics like "laughter" or visual characteristics like "smiling" but does not assess emotional weight. The moment that brought you to tears the first time you watched it is not flagged differently from a routine smile. Editor judgment carries this layer.
Story significance. Why this clip matters in the larger project, what role it plays, how it connects to other clips -- these are editorial connections that require knowing the project's intent. AI tags individual clips; the editor connects them.
Technical issues specific to this shoot. AI catches general technical issues. It does not catch shoot-specific quirks like "Camera B had a polarizer applied for this scene that needs to be matched in color."
Continuity flags. Cross-clip continuity (matching eye lines, screen direction, prop continuity) is mostly a human judgment task. AI is improving here but is not yet reliable.
The pattern: AI handles per-clip description; humans handle cross-clip context and project-specific judgment. The handoff is clean enough that the workflows divide neatly.
Step 1: Ingest Into Your AI Tool
Start with organized footage. Either drag folders into your AI tool or point the tool at your project's source media folder. Most tools support local file references (faster, no upload) or cloud upload (slower upload, faster subsequent search).
For a 100-clip project, expect 5-15 minutes for the ingest step itself, depending on file sizes and connection speed. The mechanical work matters because the AI's tags will reference these files -- so confirm everything you want analyzed is included.
What to verify before proceeding:
- All cameras are represented (Camera A, B, C if multi-camera)
- External audio recorders are included if you want them analyzed
- No duplicate copies are present (will create duplicate tags)
- No render exports or unrelated material is present (creates noise)
Once ingest completes, proceed to bulk tagging.
Step 2: Run Bulk Auto-Tagging
Trigger the AI tool's bulk analysis. This single operation runs every clip through the AI's analysis pipeline, generating descriptive tags for every clip. The work happens in the background while you do other things.
Processing time depends on footage volume:
- 30 minutes of footage: 5-10 minutes of processing
- 2 hours of footage: 15-30 minutes of processing
- 10 hours of footage: 60-120 minutes of processing
- 50 hours of footage: 4-8 hours of processing
The tool is doing several things in parallel for each clip:
Visual analysis: identifying shot type, subjects, actions, composition, motion characteristics
Audio analysis: detecting dialogue, music, ambient sound, silence, speakers
Speech transcription: generating word-level timecoded transcripts for any dialogue
Quality detection: flagging technical issues
Cross-clip indexing: building a searchable database that lets you query the entire footage set as one unit
Most tools surface partial results as soon as transcription completes (which is fast), so you can begin working with the index even while visual analysis continues. This is useful on large projects where the full processing takes a long time.
Step 3: Verify Tag Accuracy
Do not blindly trust AI tags. Spend 10-15 minutes verifying tag quality on a sample of clips before relying on the full set.
If verification reveals broad tag problems, do not proceed assuming you can fix them later. Diagnose the cause first. Common causes:
- Codec compatibility issues (the AI cannot fully analyze the format)
- Unusual content type the AI's training data underrepresents
- Audio quality problems affecting transcription
- Tool-specific bugs or limits
Fix the root cause and re-run analysis if needed. Better to spend an extra 30 minutes diagnosing than to build the rest of your workflow on a faulty foundation.
Step 4: Add High-Judgment Context
The AI tags handle the descriptive layer. The editor's job is to add the judgment layer -- the context that AI cannot infer but that matters for the project.
Walk through your selects-quality clips (typically 30-100 clips out of the larger pool) and add notes that go beyond description:
Take quality opinions. "Strong energy in the second half" or "Performance flatter than the alt take" or "Watch the eye flick at 0:45 -- distracting."
Project-specific significance. "This is the headline quote for the script" or "Use this as the closing line" or "Save for the cutdown version."
Cross-clip context. "Pairs with Camera B 14:30 for matched OTS" or "Same scene as Clip 23 but better lighting."
Editor warnings. "Audio buzz starts at 2:30 -- avoid using past that point" or "Sun flare in this take, may need to reframe."
Director notes. If the director or showrunner identified preferred takes during the shoot, capture that information.
Add these notes to the same metadata fields the AI used (so they appear together) or to a separate notes field if your tool supports it. The combined human-plus-AI metadata is what makes the rest of the workflow fast.
Time for high-judgment notes on a 100-clip project: 30-60 minutes if you focus on selects-quality clips and skip the obvious filler clips. The editor's time goes where it adds value, not on adding redundant descriptions to clips the AI already tagged correctly.
Step 5: Export Tags to Your NLE
The tags exist in your AI tool's database, but they need to be available in your NLE for them to be useful during the actual edit. Most modern AI tools export tag metadata to NLE-compatible formats.
For Premiere Pro:
- Tags appear as clip metadata (description, comments, log notes)
- Search in Project panel by tag content works directly
- Custom metadata columns can display specific tag dimensions
- Bins can be auto-organized by tag values
For Final Cut Pro:
- Tags appear as keywords on each clip
- Smart Collections can be built from keyword combinations
- Search by keyword filters libraries instantly
For DaVinci Resolve:
- Tags appear as clip metadata fields
- Smart Bins can be configured to filter by tag values
- Metadata-based search works across the project
The export usually happens automatically as part of the AI tool's project file generation. When you open the .prproj or FCPXML or .drp in your NLE, the tags are already there. Verify a few clips after import to confirm tags carried over correctly.
Step 6: Use the Tags to Edit Faster
The payoff for AI logging is in the edit, not in the prep. The time you save logging is real, but the bigger win is the editing speed that good tags enable.
What you can do with well-tagged footage:
Search by content. "Show me all wide shots with movement" returns relevant clips in seconds. Without tags, this would require scrubbing through every clip.
Filter by quality. "Show me only clips marked as usable" hides the takes you already know are problematic.
Find moments by topic. "Show me clips where the founder talks about pricing" returns transcript-matched clips. Critical for interview-driven content.
Find visual matches. "Show me clips that look like this one" returns clips with similar shot type, subject, and composition. Useful for finding alternative takes or matching B-roll.
Build sequences from search. Instead of dragging clips one at a time from bins, you can build sequences by searching for what you need at each moment.
- Search by content, not just filename
- Instant filtering by quality flags
- Topic-based clip discovery
- Visual style matching
- Faster sequence building
- Easier handoff between editors
- Scrubbing through clips repeatedly
- Losing track of which take was which
- Missing strong moments hidden in the bin
- Long handoff time when another editor takes over
- Generic edits that miss the best material
The compounding benefit is that good logging makes everything downstream faster. Selects build faster. Rough cuts build faster. Revisions are easier because you can find footage by intent rather than memory. The 30-60 minutes you spend adding judgment-layer notes pays back many times across the rest of the project.
For the broader edit prep workflow that logging fits into, see our complete edit prep workflow guide. For the rough cut work that logging enables, see our walkthrough of creating a rough cut with AI.
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Frequently asked questions
AI uses computer vision and audio analysis to generate clip-level metadata: shot type, subject, action, audio characteristics, quality flags, and speaker identification. Speech is fully transcribed with timecodes. The combined metadata is searchable, letting editors find clips by content rather than scrubbing through bins.
Shot type classification is 90-95% accurate. Subject and action tags are 85-92%. Speech transcription is 93-97% on clean audio. Quality flag detection varies by issue type. Overall, 85-95% of AI tags are accurate enough to use directly, with the editor verifying and adding context AI can't infer.
AI processes about 30 minutes of footage in 5-10 minutes, 2 hours in 15-30 minutes, 10 hours in 60-120 minutes. The processing runs in the background. Editor verification and high-judgment notes add another 30-60 minutes for a 100-clip project, replacing 8-15 hours of fully manual logging.
AI can't reliably tag take quality on subjective dimensions, project-specific context (which moments matter for the script), emotional weight of moments, story significance across clips, shoot-specific technical issues, or cross-clip continuity. These remain editor judgment work that complements AI's descriptive layer.
AI tags appear as clip metadata in Premiere Pro's description, comments, and log notes fields. The Project panel can be searched by tag content. Custom metadata columns display specific dimensions. Bins can be organized automatically by tag values. The integration is automatic when the AI tool exports a native .prproj file.