GenAi Feedback Loop

Inspiration

Generative AI has shown tremendous potential in transforming how we interact with technology. However, evaluating and improving the responses of Large Language Models (LLMs) remains a significant challenge. Inspired by the need for robust evaluation techniques, we aimed to create a solution that helps developers systematically test and refine LLM outputs through measurable feedback loops.

What it does

The GenAI Feedback Loop application evaluates LLM responses using a combination of metrics:

• TF-IDF: Measures relevance based on term frequency and document frequency.
• Levenshtein Distance: Evaluates how closely the response matches the expected output by calculating edit distances.
• BLEU Score: Assesses the fluency and correctness of LLM-generated responses against reference texts.
• BERT Similarity: Leverages deep learning to compute semantic similarity between responses and expected answers.

This combination ensures comprehensive testing, from linguistic accuracy to semantic coherence, helping users identify strengths and areas for improvement in their LLMs.

How we built it

We built the application using:

• Next.js: Used for building a dynamic and responsive frontend interface that allows users to interact seamlessly with the application. Its server-side rendering capabilities ensure faster load times and improved user experience.
• Flask: Implemented as the backend framework to handle API requests, process evaluation logic, and manage interactions between the frontend and database.
• MySQL: A relational database chosen for storing and organizing input data, evaluation results, and user-generated information securely and efficiently.
• Ollama with LLaMA 3.2: Used for generating and evaluating LLM responses, providing cutting-edge natural language processing capabilities to analyze and refine outputs effectively.

Challenges we ran into

1. Balancing precision and performance for complex metrics like BERT similarity.
2. Ensuring scalability and reliability when handling large datasets for evaluation.
3. Integrating multiple evaluation metrics while maintaining a simple and intuitive user interface.
4. Fine-tuning the evaluation process to make it applicable across diverse use cases and LLM outputs.

Accomplishments that we're proud of

• Successfully combining statistical, linguistic, and semantic metrics into a cohesive evaluation framework.
• Building an intuitive and efficient application that simplifies the feedback process for LLM developers.
• Deploying the application seamlessly on AWS, ensuring scalability and performance.
• Enabling actionable insights that help improve the accuracy and relevance of LLM responses.

What we learned

• The strengths and limitations of various LLM evaluation techniques.
• The importance of a multi-metric approach to achieve comprehensive feedback.
• Best practices for building serverless and cloud-native applications on AWS.
• How to design a scalable feedback loop that adapts to different datasets and use cases.

What's next for GenAI Feedback Loop

• Expanding the metric library to include additional techniques like ROUGE and perplexity for even more comprehensive evaluations.
• Introducing automated feedback mechanisms that fine-tune LLMs based on evaluation results.
• Adding support for multiple LLMs to compare performance across models.
• Enhancing the interface with detailed visualizations and custom report generation.

Built With

• bleu
• flask
• flask-(backend)-**database**:-mysql-**llm-and-api**:-ollama-with-llama-3.2-**cloud-services**:-aws-(lambda
• javascript
• llama3.2
• mysql
• nextjs
• ollama
• python
• python-**frameworks**:-next.js-(frontend)
• s3
• scikit-learn
• step-functions)-**libraries**:-nltk