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Show HN: I built a GPT-4 bot which builds software incrementally
The ability to synthesize a relatively short snipped of code was already demonstrated. But I thought it would be interesting to test whether GPT-4 can replace a programmer completely.
To do that, AI needs to plan its actions and work on code incrementally, one piece at a time.
The challenge is the context size: the entire code base + plan does not fit into the context.
My approach: Add only relevant parts of the code base to the context.
Specifically, AI generation engine implements two distinct phases: planning and coding.
In the planning phase, GPT-4 receives a tree of tasks and a summary of code base (list of files and their descriptions). It replies with updated tasks (i.e. it is able to create sub-tasks as needed), the task it wants to work on in the next step and a list of relevant code fragments for that task.
In the coding phase, it receives the task description (as a tree, in YAML) and relevant code fragments. It replies with new generated or updated files, code fragments, and status: Was the task done? Do we need to break it into subtasks?
In both cases bot can also show it's "observations" before the output, as I believe it helps with planning code generation/planning.
Results: Currently I have only tested extremely basic scenarios. It needs a lot of work to be usable in practice. But I'd say it seems to work more-or-less as expected.
Example 1: "Write a reddit-like backend in Kotlin, using Ktor. Start by planning and creating subtasks."
This was the entire task which bot received, no other data.
Results:
Link to output: https://gist.github.com/killerstorm/dd6e26dc80064b7fc731d583f8d740c1#file-ktor_reddit-txt-L9
In short, it formulated reasonably-sounding subtasks and started generating code, e.g. made a Post model. It was aborted at that step due to GPT-4 API failure, it's not reliable yet.
Example 2: "Write a reddit clone in TypeScript. Start by planning and creating subtasks."
Link to output: https://gist.github.com/killerstorm/e3c50bea3ca3463c8b2d947dcfd80b84
You can see more work here, but I expect that it's less interesting.
Challenges: I'd say it can work pretty well in file-at-once mode. Making _fragments_ of the file is more challenging because it's not a well-defined concept. FWIW GPT-4 largely ignored what I wrote about file fragments and made entire files at once, which was the right decision.
I will post link to script in the comment to this post.
- killerstorm 4y agoI tried three formats: 1. All-YAML 2. All-XML 3. Custom parse for code framents + YAML I first tried it on GPT-3.5 (gpt-3.5-turbo, aka ChatGPT) and it was really struggling with formatting - sometimes it got it right, sometime wrong. For GPT-4 I used custom listing-style representation and it kind of just worked. I later re-tried with YAML and XML and it seems to work quite well too. Here's the prompt for code-generation part of the "custom" variant: You're a code construction AI which creates code iteratively. You're given a list of existing code fragments and a task to work on. Files are normally broken into multiple fragments to reduce context size. The response should be in the following format: // Observations on the task and the code base, if any // A plan to implement the task // Code fragments to be added to the code base. Use the following markers to delimit code fragments. // (Normally a fragment would be a function, class, or a list of related lines) /// BEGIN_FILE <path>: <description> /// BEGIN <marker>: <description> <code> /// END <marker> /// END_FILE <path> CODE_GENERATION_STATUS: <status> # COMPLETE, PARTIAL, REDO description: <description> # updated description if the status is PARTIAL or REDO subtasks: # updated subtasks if the status is PARTIAL or REDO - id: <id> status: <status> # DONE, PARTIAL, TODO description: <description> Here's the script for "custom" variant: https://gist.github.com/killerstorm/2296b282c818ffcfe4ceb729ce639911 https://gist.github.com/killerstorm/2296b282c818ffcfe4ceb729... Note that you really need GPT-4 to reproduce the results, it doesn't really works in GPT-3.5, although you can see some activity.
- killerstorm 4y agoYAML variant actually does generation + planning as a single step. Code: https://gist.github.com/killerstorm/c8d64d29adfad55d9201caecfbdfb7a8 https://gist.github.com/killerstorm/c8d64d29adfad55d9201caec...
- cloudking 4y ago> In the planning phase, GPT-4 receives a tree of tasks and a summary of code base (list of files and their descriptions). It replies with updated tasks (i.e. it is able to create sub-tasks as needed), the task it wants to work on in the next step and a list of relevant code fragments for that task. Are you passing the original task in each prompt? If not I think that it's going to lose context of what it's trying to build overall. How are you deciding what are relevant code snippets to send?
- killerstorm 4y ago> Are you passing the original task in each prompt? Currently yes. Planning: Prompt + Code base summary + All tasks Code gen: Prompt + All tasks + Current task + Existing code All tasks include the original task. > If not I think that it's going to lose context of what it's trying to build overall. It can be a problem but there's a lot of room for improvement. An obvious optimization would be to hide subtasks of completed tasks. Then, perhaps hide subtasks until model decides to focus on a particular part of the tree. Summarization is also a possible strategy, e.g. LangChain uses that approach extensively. But I think once 32k GPT-4 it might be able to do quite a lot without these tricks. > How are you deciding what are relevant code snippets to send? Model receives a full list of files as well as fragments in each file. It can then select which fragments are needed for a selected task (in the generated output task_id is before the list of relevant files). E.g.: https://gist.github.com/killerstorm/dd6e26dc80064b7fc731d583f8d740c1#file-ktor_reddit-txt-L136 https://gist.github.com/killerstorm/dd6e26dc80064b7fc731d583... selected_task_id: t_0_2_3_2 required_code_fragments: src/models/Comment.ts: - class_definition