As LLM become part of everyday engineering workflows, it's critical to understand what they can and cannot do — especially in product engineering, where business context matters deeply.

When you ask LLM to generate code for a particular business context (something only you know entirely), LLM don't "know" your business.

LLM can only predict based on:

  • Common patterns from the code that was trained on
  • Your prompt (whatever hints and context you give to LLM)
  • General best practices

BUT — LLM cannot magically "know" your private business rules unless you clearly describe them.

LLM predict code that sounds correct. LLM don't understand or validate your real-world needs unless you explain them.

How LLM work when you ask for code

  • Prediction: It predicts the most likely following tokens that look like good code for the problem you described.
  • Pattern matching: LLM copies and adapts patterns from its training data that seem to fit your request.
  • Filling gaps: If you miss details, it's based on common sense, but that guess might be wrong.

So, how do you get reliable code from an LLM?

  • Give very detailed prompts Include your business rules, data structures, constraints, and edge cases.
  • Use critical review after generation After LLM give you the code, you must review and test it to ensure it fits your business.
  • Set up test cases Always ask for code plus tests that verify the logic according to your business needs.
  • Iterative correction You might need to correct, guide, or refine the code a few times by giving feedback ("No, this function also needs to handle XYZ").

Analogy

Asking an LLM to write perfect business code without full context is like asking a lawyer who never heard your case to write a court judgment — they'll guess based on experience, but the real facts must come from you.

Here's a simple reality check for anyone adopting AI into their engineering teams:

Expectations vs Reality

LLM writes perfect business-specific code
➡️ LLM predicts likely code patterns based on general training data.

LLM understands my business domain
➡️ LLM does not know your private business context unless you explain it explicitly.

LLM validates outputs against business rules
➡️ LLM only predicts outputs — you must validate and test separately.

LLM reduces need for strong specs
➡️ LLM needs more precise and stricter specifications to perform well.

LLM saves time without supervision
➡️ LLM amplify productivity but requires human review to ensure correctness.

LLM innovate or invent new ideas
➡️ LLM recombine existing knowledge; they don't invent beyond what they have seen.

LLM replace developers or product engineers
➡️ LLM are assistants, not replacements — judgment and domain expertise stay human-driven.

Bigger models are always better
➡️ Smaller fine-tuned models often perform better for specific business use cases.

Key Mindset Shift for Product Engineers

  • Think of LLM as prediction engines, not knowledge engines.
  • Use them for draft generation, automation, and exploration, but own the final quality yourself.
  • Training data ≠ for your company’s context.
    • If it’s not in the prompt, it’s not in the model’s head.
  • Good prompts = good outputs.
    • Better input leads to much better results.

Tasks where LLM are very effective:

Drafting code templates
➡️ Fast at generating boilerplate, CRUD operations, and API wrappers.

Creating documentation
➡️ Summarises, formats, and structures text very quickly.

Generating test cases
➡️ Can suggest unit/integration tests if logic is clear.

Exploring alternatives
➡️ Provides multiple ways to solve a coding or design problem.

Speeding up research
➡️ Quickly summarises concepts, tools, libraries, and frameworks.

Idea expansion
➡️ Good at suggesting more use cases, edge cases, or features.

Writing first drafts of emails, specs, and user stories
➡️ Useful for early rough drafts to save time.

Basic data transformation scripts
➡️ Good at SQL queries, simple ETL scripts, and data formatting.

Tasks that require human ownership:

Understanding deep business context
➡️ LLM can't "know" your company’s strategy, policies, or customer expectations.

Validating correctness
➡️ AI-generated code, tests, or documents still need human review and adjustment.

Architectural decisions
➡️ LLM can suggest, but real-world trade-offs must be handled by experienced engineers.

Security and compliance
➡️ LLM may miss critical risks unless you guide them specifically.

Creative product thinking
➡️ True innovation — new product ideas and differentiation — still requires human creativity.

Prioritisation and trade-offs
➡️ AI doesn't "feel" urgency, politics, or customer pain points like humans do.

Cultural and communication nuances
➡️ Writing for internal stakeholders, clients, or executives needs human judgment on tone and sensitivity.

One-line Summary

LLM are power tools — not decision-makers.
Use them to amplify your thinking, not replace it.

💬 I'd love to hear how you're blending LLM into your engineering workflow! What challenges have you faced, and what wins have you seen?