Video QA
Techniques for asking and answering specific questions about video content using AI — combining visual understanding with temporal reasoning to extract precise information from moving imagery.
Introduced: Video question answering emerged as a distinct discipline within multimodal AI during 2023–2024, as frontier models gained the ability to process video inputs alongside text. While academic research into Video QA dates back to datasets like ActivityNet-QA (2019) and TVQA (2018), the practical ability to upload a video and ask natural language questions about its content became accessible through models like Gemini 1.5 Pro and GPT-4o. These systems moved beyond static frame analysis to genuine temporal understanding — tracking actions across time, identifying cause-and-effect sequences, and answering questions that require synthesizing information from multiple moments in a video.
Modern LLM Status: Video QA is rapidly maturing in frontier multimodal models but remains more challenging than image-based QA due to the temporal dimension. Models must process not just what appears in a single frame but how visual elements change, interact, and progress over time. The core prompting techniques — framing precise questions, specifying temporal scope, defining expected answer formats, and grounding responses in observable evidence — are essential because vague video questions produce superficial descriptions rather than targeted answers. The principles covered here apply across educational content analysis, sports video review, surveillance interpretation, and any domain where answering questions about what happened in a video matters.
Ask Questions About What You See Moving
Video QA combines visual understanding with temporal reasoning to answer specific questions about video content. Unlike static image QA where the model examines a single frozen moment, video QA requires the model to track objects, actions, and events across time — understanding not just what is present in a frame, but what happened before, what is happening now, and what consequences follow.
The core insight is that effective video QA requires combining visual understanding with temporal reasoning to answer questions that span across frames and moments. A vague question like “What’s in this video?” produces a shallow summary. But when you specify the temporal scope, the type of information you need, and the level of detail expected, the model shifts from passive description to active investigation — scanning across the timeline to locate the precise moments and visual evidence that answer your question.
Think of it like the difference between asking a witness “What did you see?” versus “Between 2:15 and 2:30 PM, did the person in the red jacket hand anything to the person at the counter?” The first invites a rambling narrative. The second directs attention to a specific timeframe, specific subjects, and a specific action — producing a focused, verifiable answer grounded in observable evidence.
When a model receives a video with an open-ended question, it defaults to a chronological summary — narrating what it sees from beginning to end with minimal depth. Structured video QA prompts redirect this behavior by defining the temporal scope (which part of the video matters), the analytical focus (what kind of information is needed), and the evidence standard (how the answer should be grounded in observable content). The difference between a generic video description and a precise, timestamped answer to a specific question often comes down entirely to how the question itself is structured.
The Video QA Process
Four steps from video input to precise, evidence-grounded answers
Frame the Question
Craft a specific, answerable question about the video content. The best video QA questions target observable facts — actions taken, objects present, sequences of events, spatial relationships, or changes over time. Avoid questions that require knowledge the video cannot provide. A well-framed question tells the model exactly what kind of information to look for, which prevents it from defaulting to a general narration of everything it sees.
“How many times does the presenter switch from the slide deck to a live demo during this conference talk, and what topic does each demo illustrate?”
Specify Temporal Scope
Define which portion of the video the model should focus on. This can be an explicit time range, a relative reference like “the opening segment” or “the final five minutes,” or a conditional scope like “every moment where the instructor demonstrates a technique.” Temporal scoping is critical because videos can be long, and without boundaries the model may spread its attention too thin or focus on irrelevant sections, producing answers that miss the specific moments you care about.
“Focus on the segment between 4:30 and 8:15. During this section, what safety equipment is the worker wearing, and does it change at any point?”
Define Answer Format
Specify how you want the answer structured. Should the model provide a brief yes/no with justification, a timestamped list of observations, a comparative analysis of different moments, or a detailed narrative with visual evidence cited? The answer format shapes how the model organizes its analysis. A format that requires timestamps forces temporal precision. A format that requires visual evidence prevents hallucinated or assumed details from contaminating the response.
“Answer in a table with columns: Timestamp, Action Observed, Participants Involved, and Outcome. Include only moments where a clear decision or handoff occurs.”
Validate and Refine
Review the model’s response and ask follow-up questions to verify accuracy, fill gaps, or drill deeper into specific moments it identified. Validation is especially important in video QA because temporal hallucination — where the model confidently describes events that did not occur at the stated timestamp — is a known failure mode. Ask the model to re-examine specific claims, provide additional visual evidence for its answers, or compare its observations against information from other parts of the video.
“You mentioned the second speaker begins at 12:40. Go back and verify: is the speaker transition at exactly that timestamp, or does it occur earlier? Describe the visual cues that mark the transition.”
See the Difference
Why structured video questions produce dramatically better answers
Vague Prompt
What happens in this video?
This is a video of a cooking demonstration. Someone makes a dish with vegetables and sauce. They use a pan and some seasoning. The video is about 10 minutes long.
Structured Video QA Prompt
At what points in this cooking video does the chef adjust the heat, and what visual cues indicate each temperature change? List each instance with the timestamp, the direction of adjustment, and what is happening to the food at that moment.
[1:22] Heat increased to high — chef turns dial clockwise; oil in pan begins to shimmer and lightly smoke.
[3:05] Heat reduced to medium — visible flame decreases; chef adds garlic which sizzles gently rather than popping.
[5:48] Heat reduced to low — chef turns dial to minimum; sauce transitions from active bubbling to slow simmer.
[8:30] Heat turned off — chef removes pan from burner; residual steam visible as sauce settles.
Practice Responsible AI
Always verify AI-generated content before use. AI systems can produce confident but incorrect responses. When using AI professionally, transparent disclosure is both best practice and increasingly a legal requirement.
48 US states now require AI transparency in key areas. Critical thinking remains your strongest tool against misinformation.
Video QA in Action
See how structured questions unlock precise answers from video content
“Watch this 30-minute university lecture on machine learning. Identify every moment where the professor writes a mathematical formula on the whiteboard. For each formula, provide: (a) the timestamp when it first appears, (b) the formula itself transcribed into text, (c) the concept the professor is explaining when they write it, and (d) whether the professor verbally explains each variable or assumes prior knowledge.”
This prompt targets a specific, observable class of events — formula appearances on the whiteboard — and defines four precise data points to extract for each occurrence. By requiring both the visual content (the formula) and the contextual audio (the professor’s explanation), the prompt forces the model to synchronize its visual and auditory analysis. The question about whether variables are explained versus assumed adds an evaluative layer that transforms raw extraction into educational assessment, making the output useful for study guide creation or lecture quality review.
“Analyze this basketball game highlight reel. For each scoring play: (a) identify the timestamp, (b) describe the offensive formation used in the 5 seconds before the shot, (c) count how many passes occurred in the possession, (d) note whether the shot was contested or open, and (e) identify if the scorer was assisted or created their own shot. Present the results in a structured table.”
This prompt demonstrates temporal reasoning at its most demanding — each answer requires the model to look backward in time from the scoring event to analyze the possession that led to it. By specifying five distinct analytical dimensions per play and requesting a tabular format, the prompt prevents the model from producing a play-by-play narration and instead forces structured decomposition. The 5-second lookback window gives the model a concrete temporal scope, and the distinction between contested and open shots requires spatial reasoning about defender positioning relative to the scorer at the moment of the shot.
“Review this employee training video on laboratory safety procedures. For each safety procedure demonstrated: (a) provide the timestamp range, (b) name the procedure, (c) list the specific steps shown, (d) identify any steps that appear to be skipped or performed incorrectly compared to standard lab safety protocols, and (e) note whether the narrator verbally emphasizes each step or glosses over it. Conclude with an overall assessment of whether this video adequately covers the essential safety procedures for a chemistry lab.”
This prompt combines video comprehension with domain knowledge application. By asking the model to compare what it observes in the video against standard protocols, the prompt creates a gap analysis rather than a simple description. The requirement to note skipped or incorrect steps transforms passive viewing into active evaluation. The distinction between what is shown visually and what the narrator emphasizes verbally tests whether the audio and visual channels are consistent — a common quality issue in training materials where demonstrations may not match the accompanying narration.
When to Use Video QA
Best for extracting specific answers from video content
Perfect For
Extracting specific facts, formulas, or explanations from recorded lectures, tutorials, and online courses — turning hours of video into targeted study material focused on exactly what you need to learn.
Answering specific questions about whether safety procedures were followed, compliance requirements were met, or standard operating procedures were demonstrated correctly in recorded operations.
Querying game footage for specific plays, techniques, formations, or player behaviors — extracting structured analytical data from video that would require extensive manual review.
Answering targeted questions about recorded meetings or presentations — who said what, when specific topics were discussed, what visual materials were shown, and what decisions were reached.
Skip It When
If you need answers about live video streams with sub-second latency — such as real-time surveillance alerts or live sports commentary — dedicated video analytics pipelines outperform prompt-based QA approaches.
When you need exact measurements, pixel coordinates, or frame-perfect timing — such as motion capture data or VFX alignment — specialized computer vision tools provide the precision that language-based QA cannot match.
If your question is purely about what was said — with no visual component — audio prompting or speech-to-text techniques are more efficient and avoid the overhead of video processing entirely.
When the goal is to create, edit, or generate video content rather than analyze existing footage, video generation and editing prompting frameworks are the appropriate tools.
Use Cases
Where video QA delivers the most value
Lecture Analysis
Querying recorded lectures for specific concepts, formulas, or explanations — enabling students and researchers to locate and extract precisely the information they need without watching entire recordings.
Safety Compliance
Reviewing operational footage to answer whether specific safety protocols were followed, protective equipment was worn, and emergency procedures were correctly demonstrated during recorded activities.
Sports Film Review
Answering tactical questions about game footage — identifying formations, counting specific play types, tracking player positioning, and extracting structured performance data from recorded competitions.
Medical Training Review
Querying recorded surgical procedures or clinical demonstrations to verify whether specific techniques were performed correctly, instrument handling followed protocol, and sterile field requirements were maintained.
Product Demo Assessment
Analyzing recorded product demonstrations to answer specific questions about feature coverage, messaging consistency, and whether all key selling points were effectively communicated and visually supported.
Surveillance Review
Answering targeted questions about security footage — identifying specific individuals, tracking movement patterns, determining sequence of events, and locating the precise moments when incidents occurred.
Where Video QA Fits
Video QA bridges visual understanding and targeted information extraction
Video QA works best as part of a layered analysis strategy. Start with video captioning to get a broad understanding of the content, then use video QA to drill into specific moments or answer targeted questions that the caption missed. Layer in temporal reasoning when your questions involve cause-and-effect relationships or require the model to understand why something happened based on what came before. The QA format is particularly powerful because it forces both the prompter and the model to focus on specific, answerable questions rather than open-ended description.
Related Techniques
Explore complementary video techniques
Start Asking Better Video Questions
Apply structured video QA techniques to extract precise answers from your video content, or build multimodal prompts with our tools.