These agents engage in multi-turn interactions with users, where maintaining context, understanding intent, and generating coherent responses over extended dialogues pose significant challenges. To address these complexities, we've developed a sophisticated automated test framework that evaluates LLM agent interactions at scale, providing unique insights into model performance.
Are your large language models (LLMs) exhibiting inconsistent behavior day to day? You're not alone. As early adopters and developers, we understand the inherent variability of these stochastic systems. To tackle this challenge, we've built a dashboard that runs a sophisticated verification framework, testing individual and multi-turn accuracy and similarities across leading models, so we always know which one to use and when (automatically!).
In the fast-paced world of generative AI, LLMs are an essential tool - but they're just one part of a larger innovation toolkit. At Technicity, we're not just consultants offering advice, we're pragmatic builders focused on execution. We understand the inherent variability of LLMs and the challenges they pose. That's why we've developed a dashboard powered by a sophisticated verification framework to test LLM accuracy and consistency, so you always know which model to use and when.
If you're ready to move beyond fancy charts and decks to achieve tangible results with LLMs, let's connect.
Multi-turn interactions are where the real complexity of LLMs lies. Maintaining context, understanding user intent, and generating coherent responses over extended dialogues demand a rigorous approach to evaluation. To address this, we've built an automated testing framework that goes beyond simple question-answer scenarios. We assess response quality, coherence, factual accuracy, ethical considerations, and the ability to handle unexpected user input – all at scale.
Our framework employs a combination of three distinct yet complementary approaches to evaluate LLM agent performance:
- A separate LLM, trained on intent and sentiment classification, acts as a verifier.
- For each user-agent exchange, the verifier classifies the user's intent (e.g., information seeking, request) and sentiment (positive, negative, neutral) in both the user's input and the agent's response.
- This helps determine if the agent correctly understood the user's intent and responded appropriately.
- Word embeddings or sentence transformers convert user input and agent responses into numerical vectors.
- Cosine similarity between consecutive vectors is calculated. High similarity indicates a coherent conversation flow.
- By setting similarity thresholds, potential deviations from the intended flow can be flagged.
- A monitoring system tracks which data elements (e.g., database records, API calls) the agent accesses or modifies during the interaction.
- This observed behavior is compared against expected behavior based on user requests and the agent's intended actions.
- Discrepancies highlight potential errors in data handling.
