Multimodal AI Cheat Sheet

By the Syncfusion DevTutorials team · Last updated October 27, 2026

Every rule from the course on one page: tool choice, image sizing, documents, verification, review, speech, voice agents, the image studio, video understanding and generation, search, privacy and every lab target.

What you will learn

Find any rule or target from the course quickly

How to Use This Page

This page is for coming back to. Every lesson's main rule is here, as a table or a short snippet, with the lesson number. The last sections list every target from the lab, and a glossary.

Model names, sizes and prices change quickly. The numbers on this page come from the lab's seed bank and were checked when the course was written. Measure your own models before you rely on a number.

The One-Line Version

A model that can see is not a model that is right. Pick the right tool, check what it returns, and know what each choice costs.

Pick the Tool (Lesson 1)

Job	Best tool
Clean printed text	OCR model
Counting or locating objects	Object detector
Messy, mixed or unusual content	Multimodal LLM
Audio to text	Speech-to-text model

Images and Tokens (Lesson 2)

Rule	Why
Resize to the smallest size that still reads the digits	Image tokens cost money
Keep digits at least 10 px tall after resizing	Smaller, and decimal points vanish
Crop to the part you need	Fewer tokens, more detail

Documents (Lesson 3)

Rule	Why
Split long documents into pages	Tables across pages stay complete
Give layout hints for merged cells	Values stay in the right column
Parse money and dates with rules, then a schema	"1.234,50 €" is 1234.50

Screenshots and Charts (Lesson 4)

Rule	Why
Crop small error codes before reading	They are too small at full size
Mark chart values as estimates	Pixels are not exact numbers

Verify and Review (Lessons 5 and 6)

Check	Catches
Cross-check (lines add up)	Wrong totals
Format and range	Garbled or impossible dates
Grounding (re-read the box)	Misread names and digits
Confidence threshold	Values the model was unsure of

Route any failed check to a person. Show the source region beside each value. Save every correction as an Evals row.

Speech and Voice (Lessons 7 and 8)

Rule	Target
Word error rate (WER)	at most 5%
Vocabulary hints	product names at least 95% right
Normalise numbers	every number as digits
Timestamps and speaker labels	a reviewer can jump to the moment and see who said it
Stream every step	first audio within 1.5 s
Semantic endpoint	350 ms after a finished sentence, 1,100 ms after an unfinished one
Barge-in with a robust VAD	stop within 0.3 s; ignore coughs
Holding line during a tool call	no silence over 1.5 s
Hand-over	transfer to a person when asked, with a summary

Image Generation (Lesson 9)

Step	Rule
Prompt	Name objects, colours and size; negative prompt for people, logos, watermarks
Reference	Attach the real product photo
Words	Leave space; add a text layer
Model	Choose by cost, time, size and spelling; upscale small on-device images
Check	Objects, colours, words (OCR), shape, safety
Edit	One flaw: inpaint, recolour, outpaint or text layer, then check again
Publish	A person approves; attach a C2PA manifest

Video (Lessons 10 and 11)

Rule	Why
frames = seconds × fps	Each frame costs tokens
Two passes: free motion and sound detectors, then 4 fps near spikes	Finds 1 s events within 40 frames
Long clips: one key frame per minute, plus detectors	15 minutes in 39 frames
Video-native models sample about 1 fps	Fine for short clips, costly for long ones
Generate from the product photo (image-to-video)	Keeps the product on-model
Check sampled frames: identity, words, physics, flicker	One good first frame is not enough
cost = max(seconds, 8) × price per second × takes	Work it out before you generate
Visible "AI-generated" mark + C2PA	Viewers trust video

Search (Lesson 12)

Rule	Why
Words → OCR text index; looks → page-image index	Each holds what the other misses
Re-rank the top 10	Better order for little cost
Cite a page for every fact	People can check the answer

Privacy and Cost (Lesson 13)

Rule	Why
OCR and speech on the device	Photos and audio cannot be redacted with text rules
Send text, not the photo	Cheaper, and no image leaks
Redact card, email, phone and address	Nothing personal leaves the device
Blur faces and plates in photos you must send	The damage stays visible; the people do not
Keep 30 days; delete everywhere on request	Including both search indexes

const PATTERNS: [RegExp, string][] = [
  [/\b(?:\d[ -]?){12,15}\d\b/g, "[CARD]"],
  [/[\w.+-]+@[\w-]+\.[\w.]+/g, "[EMAIL]"],
  [/(?:\+44\s?|\b0)7\d{3}\s?\d{6}\b/g, "[PHONE]"],
  [/\b\d{1,4} [A-Z][a-z]+ (?:Street|Road|Lane|Avenue|Close)\b/g, "[ADDRESS]"],
];

Every Lab Target

Lesson	Target
1	Best tool for at least 8 of 10 items; cheaper than always-LLM
2	All 12 totals read, at the lowest cost
3	At least 95% of key fields right; every multi-page table complete
4	Bug-report fields at least 90% right; every chart value flagged
5	0 silent totals; at least 90% caught; at most 10% false alarms
7	WER at most 5%; names at least 95%; numbers as digits; timestamps; speaker labels
8	First audio within 1.5 s; no cut-offs; barge-in within 0.3 s; no stop for a cough; hand-over
9	A usable 1024 px image for all 6 briefs; no flawed image accepted; under $1.00; approved and labelled
10	All 6 moments within 2 s; at most 40 frames per clip
11	A clean take for all 3 shots; no flawed take accepted; under $12; marked and labelled
12	At least 80% recall on word and layout questions; citations on
13	0 leaks; under 0.5¢ per claim; kept at most 30 days; deleted on request
14	Your five answers set the targets; all five axes pass on the held-out set

Glossary

Term	Meaning
Multimodal	Takes in more than one kind of input: text, images, audio, video
OCR	Optical character recognition: reading text from an image
Image tokens	The pieces an image is cut into for the model; they cost money
Grounding	Checking a value against the place it came from
Silent error	A wrong value accepted with no warning
WER	Word error rate: word changes ÷ words said
Endpointing	Deciding the speaker has finished
Barge-in	The agent stops when the user talks
VAD	Voice activity detection: is someone speaking?
Provenance	A record of how content was made
Embedding	Numbers that stand for meaning, used for search
Recall	Right items found ÷ right items
Re-ranker	A slower model that reorders the top search results
Negative prompt	What an image model must not draw
Inpainting	Redrawing one area of an image
Image-to-video	Generating a clip that starts from a real picture
Identity drift	A generated product changing between frames
Diarization	Labelling who is speaking in a recording
C2PA	The open standard for provenance labels
PII	Personally identifiable information
Redaction	Replacing personal data with a tag
Held-out set	Test items never used for tuning