Unified diffs make GPT-4 Turbo 3X less lazy

robot flowchart

Aider now asks GPT-4 Turbo to use unified diffs to edit your code. This dramatically improves GPT-4 Turbo’s performance on a challenging new benchmark and significantly reduces its bad habit of “lazy” coding, where it writes code with comments like “…add logic here…”.

Aider’s new “laziness” benchmark suite is designed to both provoke and quantify lazy coding. It consists of 89 python refactoring tasks which tend to make GPT-4 Turbo write lazy comments like “…include original method body…”.

This new laziness benchmark produced the following results with gpt-4-1106-preview:

  • GPT-4 Turbo only scored 20% as a baseline using aider’s existing “SEARCH/REPLACE block” edit format. It outputs “lazy comments” on 12 of the tasks.
  • Aider’s new unified diff edit format raised the score to 61%. Using this format reduced laziness by 3X, with GPT-4 Turbo only using lazy comments on 4 of the tasks.
  • It’s worse to add a prompt that says the user is blind, has no hands, will tip $2000 and fears truncated code trauma. Widely circulated “emotional appeal” folk remedies produced worse benchmark scores for both the baseline SEARCH/REPLACE and new unified diff editing formats.

The older gpt-4-0613 also did better on the laziness benchmark using unified diffs:

  • The June GPT-4’s baseline was 26% using aider’s existing “SEARCH/REPLACE block” edit format.
  • Aider’s new unified diff edit format raised June GPT-4’s score to 59%.
  • The benchmark was designed to use large files, and 28% of them are too large to fit in June GPT-4’s 8k context window. This puts a hard ceiling of 72% on how well the June model could possibly score.

With unified diffs, GPT acts more like it’s writing textual data intended to be read by a program, not talking to a person. Diffs are usually consumed by the patch program, which is fairly rigid. This seems to encourage rigor, making GPT less likely to leave informal editing instructions in comments or be lazy about writing all the needed code.

Aider’s new unified diff editing format outperforms other solutions I evaluated by a wide margin. I explored many other approaches including: prompts about being tireless and diligent, OpenAI’s function/tool calling capabilities, numerous variations on aider’s existing editing formats, line number based formats and other diff-like formats. The results shared here reflect an extensive investigation and benchmark evaluations of many approaches.

The rest of this article will describe aider’s new editing format and refactoring benchmark. It will highlight some key design decisions, and evaluate their significance using ablation experiments.

Unified diff editing format

The design and implementation of aider’s new unified diff editing format helped clarify some general principles for GPT-4 code editing:

  • FAMILIAR - Choose an edit format that GPT is already familiar with.
  • SIMPLE - Choose a simple format that avoids escaping, syntactic overhead and brittle specifiers like line numbers or line counts.
  • HIGH LEVEL - Encourage GPT to structure edits as new versions of substantive code blocks (functions, methods, etc), not as a series of surgical/minimal changes to individual lines of code.
  • FLEXIBLE - Strive to be maximally flexible when interpreting GPT’s edit instructions.

A helpful shortcut here is to have empathy for GPT, and imagine you are the one being asked to specify code edits. Would you want to hand type a properly escaped json data structure to invoke surgical insert, delete, replace operations on specific code line numbers? Do you want to use a brittle format, where any mistake causes an error that discards all your work?

GPT is quantitatively better at code editing when you reduce the burden of formatting edits by using a familiar, simple, high level and flexible editing format.

Choose a familiar editing format

Unified diffs are perhaps the most common way to show code edits, because it’s the default output format of git diff:

--- a/greeting.py
+++ b/greeting.py
@@ -1,5 +1,5 @@
 def main(args):
     # show a greeting
-    print("Hello!")
+    print("Goodbye!")

Choosing such a popular format means that GPT has seen many examples in its training data. It’s been trained to generate text that conforms to the unified diff syntax.

Use a simple editing format

Aider’s previous benchmark results made it clear that simple editing formats work best. Even though OpenAI provides extensive support for structured formats like json and function calls, GPT is worse at editing code if you use them. I repeated these and other similar benchmarks against GPT-4 Turbo, and again reached these same conclusions.

Informally, this is probably because stuffing source code into JSON is complicated and error prone. Wrapping the python code print("On Windows use \"C:\\\"") as valid json is pretty painful and error prone. Due to escaping issues GPT’s code is often syntactically incorrect when it’s unpacked from JSON, or the JSON decode just fails entirely.

On the other hand, the core of the unified diff format is very simple. You include a hunk of the file that needs to be changed, with every line prefixed by a character to indicate unchanged, new or deleted lines. A unified diff looks pretty much like the code it is modifying.

The one complicated piece is the line numbers found at the start of each hunk. They look something like this: @@ -2,4 +3,5 @@. GPT is terrible at working with source code line numbers. This is a general observation about any use of line numbers in editing formats, backed up by many quantitative benchmark experiments.

You’ve probably ignored the line numbers in every diff you’ve seen, because the diffs usually still make sense without them. Aider tells GPT not to include line numbers, and just interprets each hunk from the unified diffs as a search and replace operation:

This diff:

@@ ... @@
 def main(args):
     # show a greeting
-    print("Hello!")
+    print("Goodbye!")

Means we need to search the file for the space and minus - lines:

def main(args):
    # show a greeting

And replace them with the space and plus + lines:

def main(args):
    # show a greeting

Simple, right?

Encourage high level edits

The example unified diffs we’ve seen so far have all been single line changes, which makes them pretty easy to read and understand. Consider this slightly more complex change, which renames the variable n to number:

@@ ... @@
-def factorial(n):
+def factorial(number):
-    if n == 0:
+    if number == 0:
         return 1
-        return n * factorial(n-1)
+        return number * factorial(number-1)

The following “high level diff” of the same change is not as succinct as the minimal diff above, but it is much easier to see two different coherent versions of the factorial() function.

@@ ... @@
-def factorial(n):
-    if n == 0:
-        return 1
-    else:
-        return n * factorial(n-1)
+def factorial(number):
+    if number == 0:
+        return 1
+    else:
+        return number * factorial(number-1)

Aider’s system prompt encourages GPT to produce these high level diffs. This makes GPT better at producing correct diffs, which can be successfully applied to the original file.

Experiments without “high level diff” prompting produce a 30-50% increase in editing errors, where diffs fail to apply or apply incorrectly and produce invalid code. When a patch fails, aider needs to ask GPT for a corrected version of the diff. This takes time, costs tokens and sometimes fails to produce a successful edit even after multiple retries.

There are probably a couple of reasons why high level diffs help:

  • It’s easier to produce diffs that both correctly match the original code and correctly produce the intended new code. There is less risk of GPT getting confused, compared to generating a series of surgical edits that interleave lines of old and new code.
  • High level hunks often contain more lines than a surgical hunk, so they are less likely to accidentally match unrelated parts of the code. This is helpful because GPT can’t reliably give us line numbers to specify exactly where in the file to make changes.

Be flexible when applying edits

GPT frequently makes imperfect diffs that won’t apply cleanly. They exhibit a variety of problems:

  • GPT forgets things like comments, docstrings, blank lines, etc. Or it skips over some code that it doesn’t intend to change.
  • GPT forgets the leading plus + character to mark novel lines that it wants to add to the file. It incorrectly includes them with a leading space as if they were already there.
  • GPT outdents all of the code, removing all the leading white space which is shared across the lines. So a chunk of deeply indented code is shown in a diff with only the leading white space that changes between the lines in the chunk.
  • GPT jumps ahead to show edits to a different part of the file without starting a new hunk with a @@ ... @@ divider.

As an example of the first issue, consider this source code:

import sys

def main(args):
    # show a greeting


The diff below is missing the “show a greeting” comment line, and represents a common type of mistake GPT might make. When we search for the minus - lines, we won’t find them in the original file because of the missing comment.

@@ ... @@
-def main(args):
-    print("Hello!")
-    return
+def main(args):
+    print("Goodbye!")
+    return

Aider tries to be very flexible when applying diffs, in order to handle defects. If a hunk doesn’t apply cleanly, aider uses a number of strategies:

  • Normalize the hunk, by taking the minus - and space lines as one version of the hunk and the space and plus + lines as a second version and doing an actual unified diff on them.
  • Try and discover new lines that GPT is trying to add but which it forgot to mark with plus + markers. This is done by diffing the minus - and space lines back against the original file.
  • Try and apply the hunk using “relative leading white space”, so we can match and patch correctly even if the hunk has been uniformly indented or outdented.
  • Break a large hunk apart into an overlapping sequence of smaller hunks, which each contain only one contiguous run of plus + and minus - lines. Try and apply each of these sub-hunks independently.
  • Vary the size and offset of the “context window” of space lines from the hunk that are used to localize the edit to a specific part of the file.
  • Combine the above mechanisms to progressively become more permissive about how to apply the hunk.

These flexible patching strategies are critical, and removing them radically increases the number of hunks which fail to apply. Experiments where flexible patching is disabled show a 9X increase in editing errors on aider’s original Exercism benchmark.

Refactoring benchmark

Aider has long used a benchmark suite based on 133 Exercism python exercises. But these are mostly small coding problems, usually requiring only a few dozen lines of code. GPT-4 Turbo is typically only lazy on 2-3 of these exercises: the ones with the most code and which involve refactoring.

Based on this observation, I set out to build a benchmark based on refactoring a non-trivial amount of code found in fairly large files. To do this, I used python’s ast module to analyze 9 popular open source python repositories to identify challenging refactoring tasks. The goal was to find:

  • Source files that contain classes with non-trivial methods, having 100-250+ AST nodes in their implementation.
  • Focus on methods that are part of a larger class, which has at least twice as much code as the method itself.
  • Select methods that don’t use their self parameter, so they can be trivially refactored out of the class.

We can then turn each of these source files into a task for the benchmark, where we ask GPT to do something like:

Refactor the _set_csrf_cookie method in the CsrfViewMiddleware class to be a stand alone, top level function. Name the new function _set_csrf_cookie, exactly the same name as the existing method. Update any existing self._set_csrf_cookie calls to work with the new _set_csrf_cookie function.

A simple python AST scanning script found 89 suitable files and packaged them up as benchmark tasks. Each task has a test that checks if the refactor was performed roughly correctly:

  • The updated source file must parse as valid python, to detect misapplied edits which produce invalid code.
  • The target method must now exist as a top-level function in the file.
  • This new top-level function must contain approximately the same number of AST nodes as the original class method. This ensures that GPT didn’t elide code and replace it with comments.
  • The original class must still be present in the file, and it must be smaller by about the number of AST nodes in the method which was removed. This helps confirm that the method was removed from the class, without other significant modifications.

To be clear, this is not a rigorous test that the refactor was performed correctly. But it does serve as a basic sanity check that the refactor was essentially done as a cut & paste, without eliding any code as comments. And it correlates well with other laziness metrics gathered during benchmarking like the introduction of new comments that contain “…”.

The result is a pragmatic benchmark suite that provokes, detects and quantifies GPT coding laziness.

Conclusions and future work

Based on the refactor benchmark results, aider’s new unified diff format seems to dramatically increase GPT-4 Turbo’s skill at more complex coding tasks. It also seems very effective at reducing the lazy coding which has been widely noted as a problem with GPT-4 Turbo.

Unified diffs was one of the very first edit formats I tried when originally building aider. I think a lot of other AI coding assistant projects have also tried going down this path. It seems like any naive or direct use of structured diff formats is pretty much doomed to failure. But the techniques described here and incorporated into aider provide a highly effective way to harness GPT’s knowledge of unified diffs.

There could be significant benefits to fine tuning models on aider’s simple, high level style of unified diffs. Dropping line numbers from the hunk headers and focusing on diffs of semantically coherent chunks of code seems to be an important part of successful GPT code editing (besides the relentless focus on flexibly applying edits). Most LLMs will have already seen plenty of unified diffs in their normal training data, and so should be amenable to fining tuning towards this particular diff style.