How to Create the Most Realistic AI Images Possible
Achieving true photorealism remains a major challenge in AI generation, making advanced prompt engineering and re-prompting techniques crucial for bypassing the typical plastic look.
What this lesson covers
This tutorial demonstrates advanced prompting strategies to achieve photorealistic AI images using Google Nano Banana Pro within Freepik. It covers structured prompting, camera settings, and a re-prompting workflow where reference photos are used to generate accurate prompts.
Key takeaways from the creator
AI-extracted notes, not independently verified product claims. Timestamp links let you check each point in the original video.
- 01:07 ↗
Introducing language that emphasizes realistic photo styles and camera details prevents the generic waxy look in AI images.
- 02:37 ↗
Adding dollar signs around F-stop values in prompts can sometimes yield more realistic photographic results.
- 04:00 ↗
Structured prompting involves explicitly defining camera models, raw unedited aesthetics, lighting setups, and specific subject details.
- 05:45 ↗
Re-prompting allows creators to drag a real live-action photo into an AI tool to have it describe and generate a comparable prompt for new creations.
Workflow outlined in the video
- Choose an advanced image generator like Google Nano Banana Pro inside Freepik.
- Incorporate specific camera and lens parameters, such as F-stops and ISO, into your image prompts.
- Adopt a structured prompting format that details lighting, camera gear, subject positioning, and environment.
- Use the re-prompting technique by uploading real photos to generate accurate starting prompts for your desired aesthetics.
Before you use this workflow
These notes describe the source video at its publication date. Model access, pricing, connectors and interfaces may have changed. Check the original source and the provider’s current documentation before installing an add-on, connecting an account or spending credits.
ReelStack has not independently tested this workflow. Preview one representative shot and check motion, continuity and output quality before applying it to a full production. No result, cost saving or model capability is guaranteed.
How to interpret the numbers
76,336 views captured 23 October 2026. ReelStack recommendation score: 57/100. Formula: radar-v5. This is ReelStack’s calculation, not a YouTube rating or a measure of factual accuracy.
Show the calculation
- 45% performance: 3.10× views vs 8 other channel videos observed at a similar age. View-evidence factor 99% (views / (views + 1,000))
- 25% freshness: 0/100 with a 14-day half-life
- 20% momentum: 463 views/day vs 12 channel baseline (6 comparable uploads), with the same view-evidence factor
- 10% engagement: 44/100; likes + 4× comments, smoothed with a 500-view neutral prior