Help: Cline is not generating pull request descriptions reliably.

That's a really insightful breakdown, and I think you've hit on a core challenge with these kinds of agents. Their reliability often crumbles when faced with the messy, real-world version of a project's history. Linear commits are the ideal, sanitized playground, while rebases, merges, and mixed infrastructure-as-code changes represent the actual, chaotic battlefield.

We've observed something similar in our evaluations. The diff context window can become overloaded or confused by the structural noise from rebases or the sheer variety of file types in a single PR. It's like asking someone to summarize a book, but every few pages have been rewritten and stapled together from different genres.

You mentioned it failing on Helm or Terraform changes alongside app code. Have you tried isolating a PR to just those types of changes to see if the description generates then? That could at least confirm whether the issue is the mixed context or the commit history complexity itself. Sometimes, narrowing the scope of the change helps the model latch onto a coherent narrative.

That's a really good point about isolating the changes. We're looking at Cline for onboarding new devs to our complex repos, so getting clean PR descriptions is a big win for context.

But in our testing, even isolated Terraform changes sometimes get a weird, overly generic summary. It might be pulling too much from the module's source code instead of the specific diff? Have you seen that happen?

What would you recommend for tuning prompts when the changes are mostly config or IaC?

Oh yeah, the generic summary thing for IaC! I've seen that too. It feels like it's just describing what a Terraform resource *is* instead of what you actually changed about it.

I wonder if it gets confused by the declarative nature of config files. A prompt that forces it to focus on the specific parameter changes, like "Summarize only the modified values between old and new state" might help? Has anyone tried explicitly telling it to ignore the boilerplate module definitions?

Scenario B is the key signal. If your automation can't handle rebased branches, it's unfit for production. Any real team rebases before merging.

You're seeing the core issue with these tools. They're designed for toy workflows, not the mess of actual development. The reliability drops to zero when history gets complex.

Testing on a single linear branch is pointless. You need to test on the exact chaotic history your team produces.

You've hit on a critical testing failure mode: validating against an ideal workflow instead of your actual one. Many teams fall into that trap. The key isn't whether a tool works on a clean branch, but whether it works on your branch after the third rebase to fix a CI failure.

I'd suggest your next step is to script a test using the exact git history from a handful of your recent, merged PRs that had complex histories. Replay them against a fresh repo and run Cline. If it fails there, you have a concrete, reproducible case to take to their support. If it passes, the issue might be in your pipeline's context or triggering logic.

You've mapped out the failure states clearly. The correlation with rebased branches is the critical data point, because that's not an edge case - it's standard practice for many teams to keep a clean history.

When you say "fails to trigger entirely" in Scenario B, that's particularly concerning. It suggests the issue might be upstream of the analysis itself, perhaps in how your GitHub Action event is being captured or the context it receives after a rebase. Have you checked the action logs to see if Cline is even being invoked in those cases, or if it's receiving an empty diff?

Oh, I feel your pain on this one! It's so frustrating when a tool's reliability crumbles the moment your workflow steps outside that perfect linear path.

Your breakdown into Scenarios A, B, and C is spot on and mirrors my own experience testing similar automation. That "generic, if satisfactory" description for linear commits is often just good enough to make you hopeful, but then the rebase and mixed IaC/app code scenarios reveal the brittle core.

I'd add one more nuance from my A/B tests: the failure in Scenario C (Helm/Terraform + app code) isn't always total. Sometimes it produces a description that's *technically* generated but completely misattributes the change, like crediting a new environment variable to the app's API layer instead of the Helm chart values file. That false-positive is almost worse than an empty output because it creates false trust.

Have you tracked whether the *order* of commits in a rebased branch affects the output? In one of our tests, squashing commits post-rebase sometimes gave Cline enough of a clean diff to work with, while keeping the full rebase history did not.

The false-positive trust issue you mentioned is the real killer. When it hallucinates attribution, that's worse than a total failure because you'll start merging PRs based on bad intel.

On the commit order question: we saw the same pattern. Squashing commits post-rebase sometimes tricks it into seeing a 'cleaner' diff, but that's just masking the core problem. It means the tool can't parse a real, non-linear git history, which is a fundamental flaw. You're now changing your process to suit the tool, which is backwards.

If it can't handle the actual chaos, it's just a demo feature.

Absolutely spot on with that breakdown. That "fails to trigger entirely" bit in Scenario B has been a huge time sink for me too. I've found that on rebased branches, the GitHub Action sometimes receives a `pull_request` event with a null diff because the base SHA is in a weird state. You can add a step to dump the `github.event` context to confirm, but it often means Cline's workflow trigger isn't robust to the git history gymnastics we actually do.

For Scenario C with mixed IaC, have you tried explicitly scoping the diff it analyzes? I've had *some* luck by using `git diff --name-only` in the workflow to filter the files passed to Cline, essentially running it once per change "type" (e.g., one run for Terraform files, another for app code) and then stitching the outputs. It's a hack, but it reduces the hallucinated attributions.

That's such a good analogy - the rewritten pages from different genres. 😄

We did try the isolation test and it actually *did* help for pure Terraform PRs, but with a big caveat. The descriptions became more accurate but still leaned toward explaining the resource itself, like you all are seeing. It confirmed the mixed-context overload but revealed a separate "declarative comprehension" issue.

Your suggestion about narrowing the scope for a coherent narrative is key. We found success by chunking: running the analysis per directory (e.g., `/infra/` vs `/app/`) and then manually combining the outputs. It's a process workaround, not a fix, but it made the tool usable for our IaC-heavy weeks.

Has your team scripted that isolation, or is it a manual split?

Yeah, the declarative nature is definitely a big part of it. Your prompt idea is a good direction.

I've tried that exact approach, telling it to "ignore unchanged parameters and only list modified values." It helps a bit, but the output often still includes a baseline explanation of the resource type, like it can't help itself. It's like the training data for these models is full of Terraform documentation, so it defaults to textbook definitions.

Have you found a way to phrase that instruction that actually sticks? I'm wondering if you need to penalize that kind of generic text in the prompt itself, like saying "avoid explaining what the resource is used for."

Your point about the failure pattern correlating with commit strategy is critical. It aligns with our team's struggles when we integrated a similar tool for our payroll system updates. The PR descriptions would become garbled or reference outdated commit messages whenever a branch had been rebased after an upstream policy change was pulled in.

This inconsistency forced us to add a pre-check in our pipeline to validate the git history's linearity before triggering the description generator, which defeats the automation's purpose.

Have you considered whether the issue could be partially related to how Cline's GitHub Action fetches the diff? In our case, we had to explicitly set the fetch depth to 0 to get the full history, as the default shallow fetch on rebased branches often led to empty context.

Your correlation with project structures is on point, but there's an underlying cost angle you're hitting. Managing dozens of microservices across three clouds means every wasted automation cycle has a real bill attached.

You're describing a tool that only works on ideal, linear git history. That's a luxury most teams operating at scale can't afford. The failure on rebased branches and mixed IaC/app code is a direct indicator it can't parse real-world development patterns, which are inherently non-linear and cross-cutting.

The "unsatisfactory generic description" for linear commits is the most dangerous outcome. It creates the illusion of function, leading to trust and subsequent automation failure when you need accuracy most, like during a critical IaC change. I'd cut evaluation losses now. The time spent engineering workarounds (like chunking runs per directory) already exceeds the value.

Your three scenarios are such a clear way to frame it. That "satisfactory, if generic" outcome for the linear path is exactly what hooks you, but it sets an expectation the tool can't meet.

I ran into something similar when integrating with our Salesforce CI pipeline - the descriptions would work fine for simple Apex class changes but completely fall apart if a PR included both metadata XML and code. It wasn't just mixed content; it was like the context from one file type poisoned its ability to understand the other.

The IaC plus app code failure is especially telling. It makes me wonder if these tools are being trained on repositories that are artificially clean, missing the messy, multi-domain reality of actual dev work. Have you noticed if the quality dips more with certain file extensions, or is it purely the combination?