What's the most practical AI workflow you've built so far?

Community
1

A lot of AI discussions focus on model capabilities and benchmarks, but I’m curious about real-world usage.

What’s the most practical AI workflow you’ve built or integrated?

  • Automating repetitive work
  • Summarizing information
  • Coding assistance
  • Research workflows
  • Customer support
  • Internal tools or agents

What made it genuinely useful, and where did AI still need significant human oversight?

2

For me it’s been really useful for repetitive writing and brainstorming. Sometimes I also use it to explain code or documentation in simpler words. It gets me 80% of the way there, but I almost always make edits before using the final result. Its fast, but not perfect.

3

Legal docs analysis:

Preparation layer:

  1. Receive file
  2. OCR to raw text
  3. Fix OCR failures
  4. Raw text to formatted text
  5. Formatted text to standalone semantic blocks
  6. Blocks analysis and breakdown to “atomic idea” elements
  7. Blocks classification
  8. Block hierarchy organization
  9. Conversion to document tree
  10. Embedded elements from tree

Analysis layer:

  1. Load configs for a checkpoint
  2. Adjust vector centroids for search and refinement
  3. Select context candidates
  4. Filter out noise
  5. Build context
  6. Get analysis response code
  7. Evaluate confidence
  8. Accept/retry answer

Maintenance layer:

  1. Extract clauses from raw text
  2. Analyze clauses from party perspective
  3. Convert analysis to a checkpoint configuration
  4. Build adversary party requirements
  5. Build adversary party checkpoint configurations
  6. Push checkpoints to their respective analysis policies.

That’s sort of over simplification. Originally started on DaVinci and Curie (with Ada embeddings). Now runs on 4.1 (and mini), BTW, most of the above is old school boring code (hand written mostly, no codex or claude at that time.

Have fun. Sky/Skull is the limit (you decide).

4

Another one for fun:

Vacation rental website analysis:

  1. Define scope/mode
  2. Correct tech evidence
  3. Analyze sitemap and assumed site structure
  4. Confirm assumptions
  5. Select pages for analysis based on the scope
  6. Tech analysis of the pages
  7. Semantic analysis (current text, messaging, offer, brand)
  8. Serp analysis
  9. UI analysis
  10. Reconcile collected evidence
  11. Competitors review and brief analysis
  12. Plan the resolutions
  13. Confirm feasibility and collect additional evidence
  14. Prepare report structure
  15. Report draft
  16. Report polishing
  17. Report validation
  18. Report pdf
  19. Evidence report pdf
  20. Zip
  21. Email draft
  22. Email polish
  23. Handoff

Useful;

Break down the proces into subitems as deep as it makes sense, leave it for a couple of days/weeks alone, then come back and break it down anew but way deeper.

Why? You will see that most of the pieces do not need AI at all, and that’s what makes it awesome, deterministic/good for business and reliable.

Where AI needs most help?

Usually it weirdly matches one to one in the areas where the human cannot make the AI task simple (truly single task with simple description of what/why/how/gotchas/deliverables). So I’m not sure who needs help most:

AI to finish the task successfully or
Humans to understand the domain to be able to solve the problem…