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Large-scale language models (LLMs) are amazed at their ability to infer, generate and automate, but it is not just the initial performance of the model that separates persuasive demos from permanent products. It’s how well the system learns from real users.
Feedback loops are the missing layer of most AI deployments. LLM is integrated into everything from chatbots to research assistants to ecommerce advisors, so the real differentiator lies in how effectively collect, structure and act on user feedback, rather than better prompts or faster APIs. Whether it’s a thumb, a revised or abandoned session, all interactions are data, and every product has an opportunity to improve it.
This article discusses the practical, architectural and strategic considerations behind building an LLM feedback loop. When drawn from real-world product deployment and internal tooling, we dig into how to close the loop between user behavior and model performance, and why human-in-the-loop systems are still essential in the age of generating AI.
1. Why Static LLMS Plateau
A common myth in AI product development is that you have fine-tuned the model or completed the prompt and it’s done. But it is rare to see how things unfold in production.
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LLM is probabilistic… They don’t “know” anything in the strict sense, and performance often deteriorates or drifts when applied to live data, edge cases, or evolving content. Use cases shift, users introduce unexpected phrasing and introduce small changes to the context (such as branded voice and domain-specific terminology).
The lack of feedback mechanisms means that teams will chase quality through rapid coordination or endless manual intervention. This treadmill burns time and slows down repetition. Instead, the system should be designed to learn not only during initial training, but also from continuous use via structured signals and commercialized feedback loops.
2. Feedback Types – Beyond the Rise/Down
The most common feedback mechanism in LLM-driven apps is the top and bottom of the binary thumb. It’s easy to implement, but very limited.
Feedback is best multidimensional. Users may dislike responses for many reasons. There are de facto inaccuracies, tone inconsistencies, incomplete information, and even misunderstandings of intent. The binary indicator captures none of its nuances. Worse, it often creates a false sense of accuracy for the teams analyzing their data.
To significantly improve system intelligence, feedback must be categorized and contextualized. It is as follows:
- Structured correction prompts: “What’s wrong with this answer?” Selectable options (“Factorly wrong”, “Too vague”, “Wrong tone”). You can use things like TypeForm or Chameleon to create custom in-app feedback flows without breaking the experience. On the other hand, platforms like Zendesk and Greated can handle structured classification of the backend.
- Freeform text input: Allow users to add clear modifications, paraphrases, or better answers.
- Implicit operational signals: Abandonment rate, copy/paste actions, or follow-up queries that show dissatisfaction.
- Editor-style feedback: Inline correction, highlighting or tagging (for internal tools). In the internal application, I used Google Docs style inline comments on my custom dashboard to annotate the model’s replies. This is a pattern inspired by tools such as conceptual AI and grammar, and relies heavily on the interaction of embedded feedback.
Each of these creates a richer training aspect that can inform rapid refinement, context injection, or data augmentation strategies.
3. Preserving and structuring feedback
Collecting feedback is only useful when structured, acquired and used to facilitate improvement. And unlike traditional analyses, LLM feedback is inherently troubling. It is a fusion of natural language, patterns of behavior, and subjective interpretations.
To reduce that confusion and turn it into operational one, try overlaying three key components onto your architecture.
1. Vector database for semantic recalls
When users provide feedback about a particular interaction – for example, if a response is flagged as unclear or modify some of the financial advice, they will embed and preserve the exchange semantically.
Tools like Pinecone, Weaviate, and Croma are popular for this. They allow for large semantic queries of embeddings. We also experimented with Google Firestore Plus Vertex AI Embeddings for cloud-native workflows. This simplifies searching for Firebase-centric stacks.
This allows you to compare future user input to known problem cases. If similar input is entered later, you can bring the improved response template to the surface, avoid repeated mistakes, and dynamically insert clear contexts.
2. Structured Metadata for Filtering and Analysis
Each feedback entry is tagged with rich metadata: user role, feedback type, session time, model version, environment (DEV/test/product), and trust level (if available). This structure allows product and engineering teams to query and analyze feedback trends over time.
3. Trackable session history for root cause analysis
Feedback is not in a vacuum state. This is the result of a particular prompt, context stack, or system behavior. Maps the L-log complete session trail:
User Query → System Context → Model Output → User Feedback
This set of evidence allows us to accurately diagnose what went wrong and why. It also supports downstream processes such as targeted rapid coordination, retraining data curation, or review pipelines within loops.
Together, these three components turn user feedback from scattered opinions into structured fuel for product intelligence. They make feedback scalable – and not afterthought, but the continuous improvement part of the system design.
4. When (and how) the loop is closed
Once feedback is saved and structured, the next challenge is determining when and how to act. Not all feedback deserves the same response. Some are instantly applicable, while others require moderation, context, or deeper analysis.
- Context Injection: Quick Control Iteration
Often, this is the first line of defense and one of the most flexible. You can insert additional steps, examples, or descriptions directly into the system prompt or context stack based on the feedback pattern. For example, you can use Vertex AI grounding via Langchain’s prompt template or context object to adapt to tones or scopes in response to common feedback triggers. - Fine tuning: Advanced durability improvement
If repeated feedback highlights deeper issues, such as domain understanding and outdated knowledge, it may be a time of tweaking, but cost and complexity. - Product-level adjustment: Solve with UX, not just AI
Some issues exposed by feedback are UX issues, not LLM failures. In many cases, improving the product layer can do more to increase user trust and understanding than any model adjustment.
Finally, not all feedback needs to trigger automation. Some of the highest leveraged loops include humans. Moderators are product teams who triage edge cases, tag conversation logs, or domain experts who curate new examples. Closing a loop does not always mean retraining. That means responding with the right level of care.
5. Feedback as a product strategy
AI products are not static. They exist in the messy middle between automation and conversation. This means you need to adapt to your users in real time.
Teams accepting feedback as a strategic pillar will ship smarter, safer, more human-centered AI systems.
Treat feedback like telemetry: monitor equipment and route it to parts of the system that can evolve. An opportunity for all feedback signals to improve through context injection, fine tuning, or interface design.
After all, teaching models is not just a technical task. It’s a product.
Eric Heaton is Siberia’s Head of Engineering.
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