Enhancing a Coding LLM with Robust Debugging via RLHF

Our client is a mid-sized, venture-backed AI research venture founded by a team of ex-FAANG engineers. The lab developed one of the first AI systems for automated unit test generation. Also, they created a code summarization model that could digest complex functions into simple comments (in English).

Their current focus is a coding assistant LLM for software developers with code generation, refactoring suggestions, and context-aware debugging tips capabilities. The product is delivered via an API and an IDE plugin, allowing it to integrate directly into developers’ workflows. What is more, the model helps developers identify and fix bugs by analyzing error messages, code context, and prior revisions. Our clientʼs vision is to make this AI the go-to assistant for coding and debugging, boosting developer productivity and confidence.

Our clientʼs main concern was model reliability in debugging. In complex, production-scale codebases, the LLM’s debugging recommendations often lacked accuracy and consistency. Early beta users noticed that while the model could suggest fixes for simple bugs, it struggled with nuanced or large-scale systems.

The assistant would sometimes produce hallucinations (plausible-sounding but incorrect explanations or code changes) which could send developers down the wrong path. For example, it might suggest a nonexistent API function or misinterpret a stack trace, eroding trust in its guidance.

Additionally, the LLM’s performance was inconsistent across programming languages. It excelled at debugging Python code (thanks to abundant training data in Python) but was far less reliable with languages like Java or C++, leading to uneven user experiences.

These limitations directly hindered the lab’s business goal. Their aim was to position the LLM as the industry leader in intelligent debugging assistance, differentiating it from general code completion tools on the market. Competing solutions from big tech were emerging, but none had perfected AI-driven debugging. They saw a market opportunity, yet the current state of their model put that vision at risk.

Our team started to design a comprehensive solution centered on Reinforcement Learning from Human Feedback (RLHF). RLHF is a training strategy that integrates human preferences into the model’s learning loop, ensuring the AI’s behavior aligns more closely with what expert users expect​. In this case, the goal was to align the coding LLM’s behavior with the debugging approaches of seasoned software engineers. Over a 5-month engagement, StartupSoft’s machine learning team worked hand-in-hand with the lab’s researchers to iteratively refine the LLM’s debugging skill. This meant using human feedback as a guide for the model: each time the model suggested a code fix or explanation, human experts would judge and rate these suggestions, and those judgments would directly influence the model’s further training. By continually reinforcing the patterns of good debugging behavior, the LLM could learn to prefer accurate, helpful suggestions over fallacious ones.

Our RLHF solution included:

• Extensive Human Feedback Dataset. We helped curate a dataset of 250,000+ human-annotated debugging conversations and code trace evaluations. These included real-world bug scenarios sourced from the lab’s own software and open-source project issues. For each scenario, the dataset captured the context (code snippet and any error trace), the LLM’s initial suggestion, and a human expert’s evaluation or correction. Senior software engineers from both teams were involved in labeling. They graded the LLM’s suggestions for correctness, clarity, and usefulness, and provided the “ideal” debugging responses. This trove of examples formed the backbone of the RLHF process, giving the model a wide range of debugging situations to learn from.

• Expert-in-the-Loop Feedback Loops. We established ongoing feedback sessions with the lab’s senior engineers to refine the model in iterative cycles. In practice, this meant that every week, the latest version of the LLM was tested on fresh debugging tasks pulled from real development work. Using a custom feedback platform (built by StartupSoft using React and Firebase), engineers reviewed the assistant’s live performance. They could upvote good suggestions, correct any mistakes, or flag hallucinations. These real-time debugging sessions ensured that the feedback wasn’t purely theoretical, it came directly from the target end-users (experienced developers) in real use cases. The feedback loop was two-tiered: immediate reactions via the platform, followed by deeper review meetings where we and the client’s team analyzed the model’s errors and decided on adjustments for the next training cycle.

• Tiered Reward Modeling by Bug Severity. One innovative aspect of our RLHF approach was designing a tiered reward model tailored to bug types and severity. StartupSoft’s reinforcement learning specialists trained a reward model that learned to predict the human preference rankings from the dataset. Then we went a step further by incorporating domain-specific rules so that fixing a critical bug (e.g. a security flaw or a crash in production) would yield a higher reward signal to the LLM than fixing a minor issue (like a small styling bug or a typo). In essence, the RLHF training valued solutions to high-impact bugs more heavily, aligning with the lab’s business priorities. The reward model was calibrated for nuances such as syntax errors vs. logical errors, performance issues vs. functional bugs, and so on. By weighting the rewards, the LLM learned to prioritize correctness and completeness especially when the stakes were high, making its suggestions far more reliable in serious debugging scenarios. This tiered approach ensured the AI didn’t just chase easy fixes, but truly learned what “good” looks like for hard bugs versus trivial ones.

• Iterative Training & Evaluation Cycles. Over the 5-month project, we ran continuous improvement cycles. Each cycle consisted of fine-tuning the LLM with the latest batch of human feedback (using the reward model in a Proximal Policy Optimization loop), then evaluating it against a battery of tests. We monitored KPIs at every iteration, including resolution accuracy, hallucination rate, and cross-language consistency (performance parity across Python, Java, C++, JavaScript, and other supported languages). The training was conducted on AWS multi-GPU clusters using Ray RLlib to scale the reinforcement learning algorithms. Thanks to our experiment tracking with Weights & Biases, we transparently logged each experiment’s outcomes. If a model version showed a regression in, say, Java debugging accuracy or started to hallucinate less often but at the cost of brevity, we caught it immediately and adjusted hyperparameters or dataset emphasis in the next cycle. This tight experimental feedback loop allowed us to converge on an optimal model swiftly. By the end of the engagement, we had gone through dozens of mini release cycles, each time incorporating client feedback and increasing the model’s performance step by step.

Our use of a shared feedback platform (the React/Firebase tool) meant the client’s developers were effectively co-designers of the AI’s behavior – their day-to-day experience debugging with the model directly shaped its evolution. This collaborative, transparent approach ensured that by the project’s end, the lab had not only a greatly improved LLM but also full visibility into how it was achieved and the confidence to maintain and further train it as needed.

Our collaboration resulted in improvements to the coding assistant’s debugging capabilities:

• 42% of improvement in debugging suggestion accuracy. On the lab’s proprietary benchmark of 10,000 coding issues (spanning seven programming languages), the RLHF-enhanced LLM solved or gave correct guidance on 42% more issues compared to the pre-RLHF model. This huge leap in accuracy means developers get correct solutions from the AI far more often than before, greatly increasing their trust in its recommendations.
• 63% reduction in the hallucination rate. The frequency of the LLM producing irrelevant or incorrect “guesses” dropped by nearly two-thirds. Measured on live production code inputs, the model’s hallucinations (such as suggesting non-existent library calls or misdiagnosing the cause of a bug) became much rarer. Developers using the assistant now encounter far fewer distracting or misleading answers, which streamlines the debugging process.
• 31% faster time-to-resolution for issues. Internal usability tests and beta user studies showed that with the improved LLM, developers fixed bugs 31% faster on average when using the AI assistant in their IDE. By providing more accurate hints and pinpointing root causes quickly, the LLM helped reduce the debugging time (for example, what used to take an hour might now take around 40 minutes). This speed-up was observed across different languages and types of issues, indicating a broad efficiency gain.
• +22% increase in weekly active users post-launch. Within weeks of deploying the upgraded LLM into the product, our client saw a 22% jump in weekly active users of their coding assistant tool. More developers were not only trying the assistant, but also continuing to use it regularly. This boost in engagement reflects the community’s positive response – the assistant’s new debugging savvy attracted users and kept them coming back because it was genuinely helpful.