AI Go Code: Write, Test and Review Go Programs Safely

AI Go code is a good match because Go is small, strict and consistent: a compact language with one standard formatter, a built-in test tool and a culture of simple solutions. An assistant can draft handlers, explain compiler errors, write table-driven tests and translate code from other languages. What it cannot do alone is judge concurrency, error handling and performance in your real system, so those parts need your review.

This guide shows how to use an assistant for Go code in a way that saves time without adding risk. You will see how to brief it, which tasks work best, how to ask for tests, what to check in goroutine code and how to review output before it reaches production. The examples work with any capable assistant, including Mio.

What AI Go code does well

Go programs follow patterns that models have seen many times: HTTP handlers, command-line tools, JSON encoding, database access and worker pools. Because the language has few ways to do the same thing, generated code often looks like the code a colleague would write. The standard library is large and well documented, so a good prompt can name the packages to use and avoid extra dependencies.

The strongest uses are everyday tasks: a small HTTP server with a health check, a function that parses a CSV file, a command that reads flags and prints a table, a client with timeouts and retries. Assistants are also good at explaining unfamiliar code, writing doc comments and turning a long function into smaller ones. They are weaker at design decisions that depend on your system, such as how to split services or which consistency guarantees you need.

Task	How well it works	What to check
Small handlers and CLI tools	Very well	Input validation, status codes, timeouts
Table-driven unit tests	Very well	That cases cover edge cases, not only the happy path
Explaining compiler errors	Well	That the fix matches your Go version and modules
Goroutines and channels	Mixed	Leaks, races, closing channels, cancellation
Performance tuning	Weak without data	Profiles and benchmarks from your own code
Architecture choices	Weak	Your constraints, team and operations

Brief the assistant like a teammate

The more context you give, the less generic the answer. Name your Go version, the module path, the packages you may use and the style you follow. Paste the relevant types and function signatures, not the whole repository. Say what the code must do, what inputs it receives and how errors should be returned. If you use a framework or router, name it; if not, say “standard library only”.

Write a Go function (Go 1.22, standard library only):
func ParseOrders(r io.Reader) ([]Order, error)
Input: CSV with header id,customer,amount_cents,created_at (RFC 3339).
Rules: skip blank lines; return an error that names the line number
for malformed rows; do not panic; no global state.
Then write a table-driven test with at least 6 cases, including a
missing column, a negative amount and an empty file.
Explain any assumption you made.

The last line matters. When the assistant lists its assumptions, you can see where its picture of the problem differs from yours. If you often write similar prompts, save the style rules in a project so that new requests start from the same base; our guide to projects and memory explains how.

Error handling the Go way

Go returns errors as values and expects you to handle them. Assistants know the pattern but sometimes get lazy: they ignore an error with an underscore, wrap errors without context or return a vague message. Review for these points: every returned error is either handled or passed on, the message says what failed and with which input, and the code uses wrapping so that callers can inspect the cause. The official Effective Go guide remains the best reference for idiomatic style.

Ask the assistant to explain each error path in a sentence and to list errors that callers may want to check for. Also ask it never to use panic for ordinary failures, and to add context such as the file name or the identifier. These small rules make the code easier to debug at three in the morning. If you want help with a specific failure, paste the exact error and the surrounding lines, as described in our guide to debugging code with AI.

Tests: table-driven and honest

Table-driven tests are a Go tradition and an assistant writes them fluently: a slice of cases with a name, input and expected output, and a loop that runs each as a subtest. The official tutorial on adding a test shows the basic structure. The risk is not syntax but coverage. Models tend to produce cases that confirm what the code already does, including its mistakes.

Improve the result with three requests. First, ask for tests before the implementation, so the tests describe the requirement. Second, ask for edge cases: empty input, huge input, invalid encoding, boundaries and time zones. Third, ask the assistant to explain what each case proves. Then run the suite with the race detector and coverage, and look at which lines are not covered. A test that never fails is not a safety net. For wider guidance, see our article on AI-generated tests.

Goroutines, channels and context

Concurrency is where generated Go code needs the most care. A model can produce a worker pool that compiles and passes a quick test and still leaks goroutines, deadlocks on a full channel or races on a shared map. When you review, look for these questions: who closes each channel, how do goroutines stop, is every goroutine started with a context or a done signal, and is shared data protected by a mutex or passed by message?

• Run tests with the race detector enabled and run them more than once.
• Check that every goroutine can exit when the context is cancelled.
• Avoid starting goroutines in loops without limiting how many run at once.
• Look for unbuffered channels that block when nobody reads.
• Prefer simple code with a mutex over clever channel patterns when it is clearer.

Ask the assistant to draw the lifecycle in words: “Describe how each goroutine starts, how it stops and what happens on error.” If the description is vague, the code probably is too. The standard library documentation for the testing package and the concurrency section of Effective Go are good companions for this review.

Performance questions: measure first

When code feels slow, an assistant will happily suggest changes: preallocate slices, avoid conversions, use a pool. Some are right, many are irrelevant and a few make the code worse. Go gives you the tools to decide with data: benchmarks, the profiler and the execution tracer. Ask the assistant to write a benchmark for the function you care about, run it, then paste the numbers and ask for explanations. Without measurements, optimisation is a guess dressed as advice.

A good prompt looks like this: “Here is the benchmark output before and after my change. What does the allocation count tell us, and which line is the most likely cause?” The model can read profile summaries well and point at suspects, but you must confirm each theory with a new measurement. Keep every optimisation that does not improve a number out of the codebase; simple code is worth more than a few nanoseconds.

The same discipline applies to memory and to database calls. If a handler is slow, the cause is often a query, not the language. Ask the assistant to suggest where to add timing logs, and read the results yourself before you rewrite anything.

Reviewing, formatting and linting

Treat generated Go like a pull request from a new colleague. Run the formatter and the vet tool, then a linter your team uses, and build with the same flags as your pipeline. Read the diff, not just the final file. Check imports, because assistants sometimes invent a package or use an old import path. Check the module file, because it may add dependencies you did not ask for. Our guides to code review with AI and AI security review of code give checklists you can paste into your own process.

Security deserves a separate look: input validation, SQL queries built from strings, path handling, secrets in logs and default timeouts on HTTP clients. Ask the assistant to review its own code for these issues, then verify the answer yourself. A second look from a human is not optional for authentication, payments or anything that touches personal data.

Learning Go with an assistant

An assistant is also a patient tutor. Ask it to explain a snippet line by line, to compare two ways of solving the same problem or to quiz you on pointers and slices. Ask for the reasoning behind idioms, such as why interfaces are small or why errors are values. Then type the examples yourself instead of pasting them, and change them until you understand what breaks. Our guide to learning to code with AI describes a routine that builds skill rather than dependency.

Working with Ask Mio on Go

Mio has a Code mode for working code, bug fixes from a pasted error and refactoring. You never pick a model: Mio routes each request to a suitable one. On the Coding and Business plans, a code execution sandbox lets you run small snippets, which is useful for checking a function before you copy it into your project. For larger codebases, upload files on a paid plan and ask questions about them, or work in a project that stores your style rules.

Even with a sandbox, your own toolchain is the final judge: your Go version, your modules, your tests and your pipeline. Compare plans on the pricing page to see which one includes Code mode. Chats and files are not used to train models, and you can export or delete them at any time. Do not paste secrets, keys or customer data.

Frequently Asked Questions

Can AI write production-quality Go code?

It can write a good first draft of many everyday functions, handlers and tests. It cannot know your system, load or failure modes. Production quality comes from review, tests, the race detector, linters and monitoring. Treat the assistant as a fast junior colleague whose work always gets checked.

Is AI good at Go concurrency?

It knows the patterns but often misses details such as who closes a channel, how goroutines stop and where races hide. Always run tests with the race detector, ask for a written lifecycle of each goroutine and keep the design simple. If you cannot explain the code, do not ship it.

How do I stop the assistant from adding dependencies?

Say “standard library only” in the prompt and list any packages you allow. After generation, inspect the imports and the module file. If something unfamiliar appears, ask why it is needed, or ask for a version that avoids it. Fewer dependencies mean fewer updates and fewer security risks.

Should I let AI write my tests?

Yes, with care. Ask for table-driven tests, request edge cases and have the assistant explain what each case proves. Then run the suite with coverage and the race detector. Remember that tests written after the code can simply confirm its mistakes, so write the requirements first.

Can I use AI to learn Go from scratch?

It works well as a tutor: ask for explanations, small exercises and reviews of your own code. Type the examples instead of pasting them, and use the official documentation alongside. The goal is to understand the code, not only to collect working snippets.

Does Mio run Go code?

The Coding and Business plans include a code execution sandbox for running small snippets. What it supports can change, so test a small sample first, and always confirm important code with your own toolchain, versions and pipeline before relying on it.

The Bottom Line

AI Go code saves time on handlers, tests, explanations and refactoring, and it stays safe when you give it clear constraints and review the result like any other change. Run the formatter, vet tool, tests and race detector, read the diff and keep humans in charge of concurrency, security and design. Try it on the free plan and see which plan adds Code mode on the Ask Mio pricing page.