User Feedback in LLM-Powered Applications

Building LLM-powered applications is challenging. But the most important challenge is when products are not useful to their users. This presentation is about the different ways you can gather feedback to improve LLM applications. I’ll review the state of the art, offer some practical tips, and share some examples.

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Introduction

Based upon recent work for a client of Winder.AI, this presentation explores the user interface (UI) and experience (UX) of feedback mechanisms in applications powered by large language models (LLMs).

Ultimately the goal of any product is to provide value to the user. But no product can deliver lasting value unless it actively captures and leverages user feedback. This challenging in LLM applications because of the uncertainty in the correctness of the response. Non-standard UX paradigms are required to capture actionable insight.

The video above and the following sections outline ideas and engineering strategies for integrating these insights into robust AI products.

Why Feedback Matters

Products are built for users. Initially user feedback is often collected manually, but at scale, systems replace informal collection processes. By understanding whether an AI-powered application meets expectations, product teams can iterate toward solutions that truly empower end users. Capturing genuine user preferences uncovers pain, which can be used in future iterations or in new products.

Capturing Feedback

Common feedback strategies fall into five core paradigms.

Inline Feedback

Lightweight signals such as thumbs-up and thumbs-down icons require minimal UI real estate and are instantly familiar to users. Their ubiquity across chat platforms like ChatGPT, or Claude, demonstrates their ease of adoption. Although their coarse nature demands complementary metadata like dropdown categories or free-text explanations, to clarify why a response was deemed unsatisfactory.

Freeform Feedback

Within the chat interface itself, the LLM can prompt the user for richer commentary immediately after a thumbs-down event. This conversational loop keeps users in context, lowers friction for detailed input, and can drive automated workflows-such as routing unhappy users to a human agent or re-feeding the LLM with corrective examples.

Implicit Feedback via Editing

When users revise the LLM’s output by correcting prompts, fixing code or improving formatting, they generate ground-truth data at no extra cost. Recording those edits provides high-value training pairs for downstream fine-tuning, effectively capturing “this is what I actually wanted” without explicit rating forms.

Retrospective Feedback

Common in chatbot support scenarios, retrospective surveys (net promoter scores, overall sentiment ratings, open comments) invite users to review the entire interaction once it concludes. Although less granular than inline controls, these holistic impressions highlight systemic issues and guide broader UX and model adjustments.

Differential Feedback

By presenting multiple response options side by side, using an A/B test baked into the interface, for example, users can click their preferred version. Early ChatGPT interfaces and specialized tools (like model-ranking websites) have leveraged this to derive ELO-style ratings, quantifying comparative preferences between models, prompts, or stylistic variants.

Analysing Feedback

Capturing feedback is one thing, but how do you analyse it for information or signals?

Turning Feedback into a Feature

Feedback mechanisms themselves can become a value-add. Notifying users when the model “thinks,” revealing retrieval citations in retrieval-augmented generation (RAG) pipelines, or offering stylistic controls (as in Claude) makes AI usage more transparent and trustworthy.

Evaluating and Filtering Feedback

Once amassed, feedback must be triaged. For early proof-of-concepts, human review surfaces domain insights that no automated judge can match, although it does not scale beyond hundreds of cases. At scale, a secondary LLM can serve as a “judge,” applying custom prompts to assign quantitative scores. Bespoke workflows-for example, automated escalation triggers when sentiment dips-add sophistication but come with development overhead. Throughout, governance considerations such as audit trails, GDPR “right to be forgotten,” and compliance with emerging regulations (e.g., the EU AI Act) mandate rigorous QA and traceability.

Monitoring and Experimentation

Aggregated feedback signals fuel production monitoring dashboards, alerting teams to quality degradation or spikes in negative reactions. These same metrics underpin A/B testing frameworks: by routing subsets of users to control or experimental models and comparing their feedback curves, teams can make data-driven product decisions.

Feedback Data Pipeline

A generic feedback data pipeline would look something like this:

1. Collection: Capture raw signals (text, ratings, edits) alongside metadata (timestamps, user IDs, context).
2. Labeling: Enrich records with categories-issue type, sentiment, relevance flags-via automated tagging or human annotation.
3. Cleaning: Remove noise and personally identifiable information, filter out spam or overly generic comments.
4. Extraction: Pull out the precise elements needed for downstream applications, whether driving analytics dashboards, feeding prompt-optimization tools, or training models.

Iterative Improvement Strategies

Once feedback is understood and is being collected in a repeatable way, you can start to incorporate feedback to improve your products.

Prompt Refinement and Optimization

Manual prompt engineering remains the most immediate way to achieve quick wins. Building evaluation datasets and automated pipelines ensures that enhancements do not regress model capability over time. Emerging tools like DSPy aim to optimize prompts algorithmically, though they currently shine on simpler tasks.

Curated RAG Context

By maintaining repositories of “good” and “bad” examples, RAG pipelines can retrieve user-specific or issue-specific exemplars to steer the LLM toward preferred behaviors. This “few-shot” approach fosters the illusion of personalization and demonstrates rapid iteration on user preferences.

Fine-Tuning with Real-User Signals

When users supply ground truth (e.g., corrected outputs), those examples feed supervised fine-tuning loops. More abstract feedback-ratings, free-text comments-can train reward models in an RLHF (reinforcement learning from human feedback) framework. In practice, teams build a separate reward model to score responses and then apply reinforcement learning to align the base LLM with user-driven reward signals.

Measurement and Continuous Iteration

Any improvement effort must be paired with robust evaluation techniques: regression tests guard against new failure modes, while continuous monitoring platforms observe live performance of updated models. Differential feedback also resurfaces here as an ongoing A/B mechanism, enabling direct comparison between older and newer model versions. This cycle of capture, analysis, intervention, and reassessment underpins a true product mindset.

Conclusion and Key Takeaway

Real-world user feedback is a competitive advantage. It helps you build better products, which more people will want to buy. By thoughtfully designing feedback loops, operationalizing their data pipelines, and embedding insights into model and UX improvements, organizations can surpass generic LLM offerings and deliver genuinely valuable AI experiences. Continuous iteration, anchored in user voice and transparent processes, remains the cornerstone of effective AI product development.