Before ChatGPT, developers wrote and revised code using programming knowledge, documentation, editors, compilers or interpreters, debugging tools, tests and help from other people. They did not memorise every API or write every component from scratch.
The central loop was already describe the problem, implement a small change, run it and investigate the result. AI can accelerate parts of that loop, but it does not remove the need to understand or verify the software.
Documentation supplied the details people did not memorise
Developers learned language concepts and consulted references for syntax, library functions and framework behaviour. Manuals, books, tutorials and examples served different purposes.
A reference answered a precise question about an API; a tutorial explained a route through a concept. Copying an example still required adapting it to the actual environment and checking its assumptions.
The official Python language tutorial is a current example of that learning structure. Its own introduction distinguishes learning Python from learning programming for the first time.
I would still use authoritative documentation when an exact function signature, version change or edge case matters. A generated explanation can help interpret the page without replacing it.
Editors and development environments already provided assistance
Syntax highlighting, completion, navigation, refactoring and error diagnostics existed independently of conversational AI. Those tools reduced repetitive work while leaving the developer responsible for the change.
A compiler or interpreter also supplied feedback. A syntax error was not a personal verdict; it was information about what the program could not parse or execute.
Developers used small experiments to understand unfamiliar behaviour. A short isolated example made a language or library question easier to inspect than a large application.
The useful distinction is deterministic or context-aware tooling versus generated suggestions. Both can help, but neither turns an unverified design into correct software.
Debugging was an investigation
A developer reproduced the failure, read the error or unexpected output and narrowed the cause. Logging, breakpoints and variable inspection helped connect what the program did with what the developer expected.
A debugger could pause execution, step through code and inspect state. A minimal reproduction could remove unrelated parts until the fault became understandable.
Changing random lines until the problem appeared to vanish was never a reliable substitute for identifying the cause. It could leave an intermittent bug or hide a failure without correcting it.
My recommendation is to retain that investigative habit with AI. Give an assistant the actual symptom and evidence, then verify the proposed explanation against the running program.
People reused libraries and learned from examples
Software development has long relied on existing libraries, frameworks, sample code and shared conventions. Most application developers did not implement every parser, database or networking component themselves.
Reuse still involved judgement: does the component fit the task, how is it licensed, is it maintained and what assumptions does it make?
A search result or forum answer could provide a clue, but its version and context needed checking. A solution for another operating system or library release could be misleading.
AI changes how an example is retrieved or assembled, but the questions about compatibility, security and maintenance remain. Treat generated code as a proposal to review rather than a source of automatic authority.
Collaboration supplied another kind of intelligence
Developers asked colleagues, joined mailing lists and forums, reviewed each other’s changes and discussed design trade-offs. A useful question included the goal, relevant code, error and what had already been tried.
Code review could catch a misunderstanding of the requirement even when the program ran. Another person could also ask whether a large change was necessary at all.
Version control recorded changes and made experiments easier to compare or undo. It supported collaboration and accountability, not merely backup.
I would preserve that review layer for consequential software. An assistant can prepare a patch and explain it, but a human team still needs to own the requirements and the decision to ship.
Tests checked behaviour that a successful run could miss
A program starting successfully does not establish that it handles an empty input, a failed network request or an invalid record. Tests made those expectations explicit and repeatable.
Developers used unit, integration and other checks according to the system. The useful question was what behaviour a test established, not how many test files existed.
A test could also be wrong. Reviewing expected results and reproducing a bug remained important. That is still true when an AI writes both the implementation and its tests.
I would prefer a small set of meaningful checks over a large generated suite that mirrors the implementation without challenging it.
What actually changed with conversational coding tools?
An assistant can turn a question into an explanation, suggest a patch and, with appropriate tools, inspect files and run commands. That can reduce the friction between an idea and an initial implementation.
It can also invent APIs, miss project constraints or make an error look more coherent than it is. Those risks make the old verification skills more valuable, not less.
People coded before ChatGPT by understanding problems and using tools, references and collaboration. The strongest modern workflow keeps those foundations and uses AI to reduce effort where its output can be checked.