How do you make an AI explainer video that's factually accurate?

Anchor the facts before you generate any visuals: write the narration through a knowledge-grounded model — Google Omni's Gemini intelligence layer pulls real scientific and anatomical facts directly into the script — review that script yourself before rendering, present data as in-frame tracked text overlays, and verify every number in the finished clip against your source.

Step 1 — Generate the script through a knowledge-grounded model. Google Omni sits on Gemini's internet-scale knowledge layer, which means the model can write explainer narration with real anatomical and scientific facts instead of inventing plausible-sounding ones — it effectively removes the separate research step. invideo is an agentic video creation tool with all current models available, so you can run this whole pipeline — script, review, generation — in one place, with the invideo agent routing each step to the right model.

Step 2 — Review the narration before you render. Read every claim and number in the generated script and check anything load-bearing against a primary source; a script is cheap to fix, a rendered clip is not. Hallucination in AI explainer output is a widely reported problem — builders on Reddit describe grounding videos in source documents specifically to solve it — so treat human review as a mandatory gate, not an optional pass.

Step 3 — Put the data on screen as tracked text, not just in the voiceover. Prompt for motion graphics with keyframed, in-frame text tracking so statistics appear as overlays tied to the moving subject — in testing, Omni rendered a mock explainer stat ("47% increase in workplace happiness") as a consistent, accurate on-screen graphic. Visible numbers are auditable numbers: anyone reviewing the cut can check the overlay against the script line by line, and text consistency across frames is one of Omni's documented unlocks.

Step 4 — Keep narrated segments inside the reliable lip-sync window. If a presenter speaks on camera, structure the narration into clips of 6–7 seconds or less — across 30+ test outputs, that was the consistent ceiling before lip sync degrades, and generation lengths run 4, 6, 8, or 10 seconds, so plan sentences to those cuts. Use a single narrator per frame: multiple people speaking in one frame remains a weakness across every current AI video model.

Step 5 — Verify the rendered output, especially physical demonstrations. Check every on-screen figure, label, and diagram against your reviewed script before publishing, and add visible source attributions where credibility matters. For clips showing physical processes, Omni's physics adherence is strong on standard prompts but complex physics prompts land correctly only about half the time — so watch demonstrations specifically for physical accuracy, and regenerate the ones that miss. If a segment needs to run longer than one clip, note that scene extension currently works only on Veo 3.1-generated clips, not Omni output — a reason to route long continuous segments to Veo 3.1 and data-overlay segments to Omni.

Watch some of these to see what works for you:

In our research we found that 6 or 7 seconds is kind of the ceiling of where the model is going to perform consistently well.

— invideo's creative team