Tip 1: Gather background knowledge before implementation.

Before writing any code, you need sufficient background knowledge to evaluate whether the code the AI produces is correct, appropriate, and aligned with field conventions. Understand data shapes, missing data patterns, field-specific libraries, and existing implementations that could serve as models. You don’t need to be an expert initially; use AI to help research domain standards, available datasets, common approaches, and implementation patterns before diving into coding. This reconnaissance phase prevents you from reinventing wheels or violating field conventions. Share your current understanding level with the AI and iteratively build context through targeted questions about tools, data structures, and best practices, asking for specific references and paper summaries. This upfront investment ensures that your code aligns with community standards and handles real-world data appropriately.

Tip 2: Define your problem and distinguish problem framing from coding.

Framing a problem in a programmatic way and coding are not the same thing [20]. Programmatic problem framing is problem solving: understanding the domain, decomposing complex problems, finding the right levels of abstraction, designing algorithms, and making architectural decisions. Coding is the mechanical translation of these concepts into executable syntax in a programming language. AI tools excel at coding tasks, generating syntactically correct implementations from well-specified requirements, but they currently require human guidance for programmatic problem framing decisions that involve domain expertise, methodological choices, and scientific reasoning. Concretely, this means being able to specify inputs and expected outputs, key constraints and edge cases, and what success looks like before any code is written. You can’t effectively guide or review what you don’t understand, so establish fluency in at least one programming language and fundamental concepts before leveraging AI assistance. This foundation allows you to spot when generated code deviates from best practices or introduces subtle bugs. Without this knowledge, you’re essentially flying blind, unable to distinguish between elegant solutions and convoluted workarounds.

Tip 3: Choose appropriate AI interaction models.

It’s tempting to use the AI tools to independently generate a complete codebase, but one quickly ends up being separated from the code and making mistakes. A pair programming model, where one directs interactive AI assistants through comments in the code, can be a way to stay in close touch with the evolution of the codebase. The utility of different language models and tool types will depend on the specific development tasks, developer preferences, and project constraints. A summary of different interaction paradigms, as well as their strengths and limitations in 2025, are provided in Table 1. Because conversational tools, IDE-integrated assistants, and autonomous agents often bundle support for multiple languages and frameworks, performance can vary in ways that are not always apparent from general descriptions or benchmarks. Before committing to a tool, we recommend learning how to properly use the tool and testing it on a representative sample of your own tasks to assess its suitability for your workflow.

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Table 1. AI-assisted development tools are categorized by interaction model and deployment scenario. Each paradigm offers distinct advantages for different phases of software development, with trade-offs between automation level and developer control. These categories are illustrative: many tools increasingly span paradigms, capabilities evolve rapidly, and some listed tools remain in active development or preview. Examples are not endorsements, and reflect our current understanding of the landscape.

https://doi.org/10.1371/journal.pcbi.1014428.t001

Tip 4: Think through and divide an implementation plan.

Once you have framed your problem, translate it into a concrete architectural plan before writing any code. Providing the AI with background context (Tip 1) and architectural context (Tip 2), including details about data flow, component interactions, and expected interfaces, transforms it from a code generator into an architecture-aware development partner, generating code that fits naturally into your project rather than producing isolated solutions. Once you have a clear architectural picture, divide the work into discrete, independently testable phases before engaging the AI. Working in phases keeps individual AI sessions focused and short, reducing the risk of context degradation and making it easy to revert to a known-good state if a session goes wrong. You can use LLMs to help generate externally-managed context files, and also look at GitHub Spec Kit for specification-driven workflows that define project requirements and gated phases (Specify, Plan, Tasks). Go through this entire tip list for each chunk sequentially, avoiding the temptation to implement multiple chunks at once, and regularly cache your results (e.g., via git pushes) as you complete steps.

Tip 5: Manage context strategically.

Context (which for LLMs refers to all of the information currently in the model’s equivalent of “working memory”) is everything in AI-assisted coding. Provide all necessary information upfront for a development phase through clear documentation, attached references, or structured project files with dependencies included. Don’t assume the AI retains perfect context across long conversations; explicitly restate critical requirements, constraints, and dependencies when interactions get complex. Keep track of context and clear or compact when it’s getting close to limits. Use externally-managed context files to keep important context available across sessions while minimizing irrelevant details that can degrade AI performance (see S2 Text). Agents can effectively use these files to keep important things in context for every session. It’s also useful to keep a problem-solving file, where you can add problems whenever you notice them, and where the model can keep track of its progress.

Tip 6: Meticulously verify AI assistance for testing.

A characteristic failure mode of AI-assisted testing is the generation of “paper tests”: models will often modify or generate tests to pass rather than to validate correctness, whether during implementation or when asked to audit coverage. Coding agents may generate placeholder data or mock implementations that merely satisfy the test structure without validating actual logic, inserting fabricated input values or dummy functions that appear to meet acceptance criteria but do not reflect true functionality. These paper tests can be dangerously misleading, seemingly passing while masking broken or incomplete logic. This failure mode arises in two distinct contexts: when the AI is generating implementation code and modifies your tests to pass rather than fixing the underlying code, and when the AI is asked to generate or expand tests directly and produces tests that are structurally valid but logically hollow. In both cases, the defense is the same: always read AI-generated tests carefully and verify that they are testing what you think they are testing, and wherever possible write your own tests before requesting implementation so that the AI must satisfy your criteria rather than criteria it has invented for itself.

Tip 7: Use tests to specify and constrain AI-generated code.

AI will respond better to specific test scenarios than vague functionality descriptions. Framing your test requirements as behavioral specifications before requesting implementation forces you to articulate expected inputs/outputs, edge cases, and failure modes upfront [13]. By providing comprehensive test specifications, you guide the AI toward more robust, production-ready implementations. AI tools (such as chatbots or GitHub’s Spec Kit) can help develop these specifications in a way that will optimally guide the model. In addition, whenever a bug is identified during your development cycle, ask the model to generate a test that catches the bug, to ensure that it’s not re-introduced in the future.

Tip 8: Leverage AI to audit and expand test coverage.

AI is exceptionally well-suited to auditing your existing coverage: it can systematically enumerate boundary conditions, failure modes, and corner cases that humans tend to overlook. Feed it your function and ask it to generate tests for boundary conditions, type validation, error handling, and numerical stability. Ask it what sorts of problems your code might experience issues with, within your specified API bounds, and why those might (or might not) be relevant to address. AI can help you move beyond testing only expected behavior to robust validation that includes malformed inputs, extreme values, and unexpected conditions. Additionally, you can use AI to review your existing tests and identify gaps in coverage or scenarios you haven’t considered. The AI can help you implement sophisticated testing patterns like parameterized tests, fixtures, and mocking that would be tedious to write from scratch. If you anticipate having future collaborators for your project, you may find it helpful to prioritize building testing infrastructure early. This often includes automated validation workflows, wherein you are able to test your code automatically as you integrate changes into the broader project. AI excels at generating the boilerplate for many of these sophisticated testing tools (such as GitHub Actions, pre-commit hooks, and test orchestration) that ensure your code is validated on every push.

Tip 9: Monitor progress and know when to restart.

Unlike a human collaborator, an AI agent will not self-flag when it is going in the wrong direction: it will confidently continue making changes, consuming time and tokens, with no indication that it has gone astray. You need to actively monitor what the AI is doing: Is it changing things you didn’t want changed? Is it ignoring the changes you actually requested? Is it introducing new problems while trying to fix old ones? When you notice the AI heading in the wrong direction, stop it rather than letting it continue down an unproductive path. Further, sometimes the most efficient approach is recognizing when a conversation has become too convoluted with failed attempts. When this happens, review your prompt history to identify what went wrong: Were requirements unclear? Did you add conflicting constraints? Did you forget to specify critical details upfront? Starting fresh with these lessons learned often produces better results than continuing to debug within a polluted context. Clear the context and restart from externally-managed context files after adding additional details to prevent the same problem from occurring in the future. This also highlights the need for good version control; if you commit code before undertaking a major change, it’s easy to simply revert to the previous commit and start over if the model goes astray. Fortunately coding agents are generally very good at writing detailed commit messages, making a commit as easy as prompting “commit this to git”.

Tip 10: Critically review generated code.

Be skeptical about AI’s claims of success; unlike a human engineer who will typically flag uncertainty or incomplete work, models tend to claim success even when they haven’t really solved the problem. You always need to test the solution independently. Read and understand the code to ensure it solves problems in ways that make sense for your domain and match your prior expectation of how the problem should be solved (e.g., how you anticipated a solution looking based on your pseudocode or architecture schematics you developed in Tip 4). AI-generated code requires careful human review to ensure scientific appropriateness, methodological soundness, and alignment with domain standards. As AI tools accelerate code production, the importance of human peer review has grown rather than diminished: human reviewers often provide qualitatively different and complementary feedback to AI agents, particularly for contextual and domain-specific concerns [21]. Where possible, peer review of AI-generated code by collaborators or lab members therefore provides a valuable additional layer of quality assurance.

Tip 11: Refine code incrementally with focused objectives.

Asking an AI to broadly “improve my codebase” is particularly hazardous because the model has no persistent memory of prior architectural decisions: it will make changes that are technically plausible but may silently violate constraints, naming conventions, or design patterns established earlier in development. Instead, approach refinement incrementally with clear, focused objectives. Be explicit about what aspect you want to improve: performance optimization, code readability, error handling, modularity, or adherence to specific design patterns. When you recognize that refinement is needed but can’t articulate the specific approach (for instance, you know certain logic should be extracted into a separate function but aren’t sure how), use AI to help you formulate concrete objectives before implementing changes. Describe what you are trying to achieve and ask the AI to suggest specific refactoring strategies or design patterns that would accomplish your goal, applying the same mindsets delineated in Tips 1–10 to help you along the way.

AI excels at identifying opportunities for refactoring and abstraction, such as recognizing repeated code that should be extracted into reusable functions or methods, and detecting poor programming patterns like deeply nested conditionals, overly long functions, tight coupling between components, and sloppy or inconsistent variable naming conventions. When requesting refinements, specify the goal (e.g., “extract the data validation logic into a separate function” rather than “make this better”) and verify each change against your tests (while expanding your testing as you iterate to reflect updates and improvements) before moving to the next improvement. This focused approach prevents the AI from making changes that, while technically sound, don’t align with your project’s architectural decisions. Note that AI can inadvertently break previously working code or degrade performance while making stylistic improvements. Always test thoroughly after each incremental change, and revert if the “improvement” introduces problems or doesn’t provide clear benefits.

Tip 12: Document your code for reuse and publication.

Writing code that works for you today is not the same as writing code that others can understand, run, and build upon. AI tools can accelerate this finalization step substantially. When preparing AI-assisted code for sharing or publication, work through the following checklist:

1. Document the AI workflow. Record the AI tool(s) and version(s) used, the general prompting strategy (e.g., whether context files or autonomous agents were employed), and any persistent context files shared with the model. See the Limitations in Section 3 for a conceptual caveat about reproducibility.
2. Document the code itself. Every public function and class should have a docstring describing its inputs, outputs, and behavior. Non-obvious implementation choices, especially those that reflect a specific methodological decision, should have inline comments explaining the reasoning.
3. Specify the environment. Include a requirements file, environment.yml, or equivalent that pins dependency versions, so that others can reproduce the computational environment your code was developed and tested in.
4. Provide a README. Describe what the project does, how to install it, and how to run it. A brief description of the project structure helps new users orient themselves quickly.
5. Include working examples. At least one runnable script or notebook should demonstrate typical usage with realistic inputs and show the expected outputs. Examples are often the first thing a new user reaches for, and they serve as an additional integration test.

AI makes it substantially easier to meet these standards than it was previously: generating docstrings, scaffolding READMEs, and producing example notebooks are all tasks where AI excels. This checklist is a starting point; field- and journal-specific requirements may go further, and we recommend consulting relevant guidance such as Nature’s Code and Software Submission Checklist [22] or resources focused on reproducible scientific computing [6,23] where applicable.