Improve the performance of retrieval-augmented generation (RAG) models by validating retrieved content and aligning responses with user intent. Increase user satisfaction and avoid negative outcomes with accurate, relevant responses.
RAG embeddings are an efficient way to enhance a pre-trained model with additional domain-specific content, without the extensive effort of building models from scratch. They are particularly effective for applications that require dynamic content, such as changing compliance laws or product catalogs. However, retrieving data from outside data sources can affect the quality and relevance of responses, resulting in errors and hallucinations that erode user trust and share misinformation.
To address these challenges, Sama experts can help measure and improve end-to-end model performance when using RAG embeddings, plus:
Without evaluation, RAG models risk retrieving incorrect information, resulting in hallucinations or misleading responses. By validating retrieval accuracy and ranking responses, we help ensure that outputs are reliable and relevant — especially when trustworthy information is critical, such as customer support, healthcare, or legal applications.
Unvalidated RAG models may not understand nuances in the conversation, including underlying context and how it changes over time. By validating that the retrieved content directly supports the query’s context, we can improve the model’s ability to handle ambiguity and multi-turn conversations.
RAG models often miss the mark on user intent, leading to irrelevant content or off-topic information. By refining retrieval criteria and ranking retrieved content by relevance, we can help minimize irrelevant content that doesn’t directly answer the user’s question.
Partnering with Sama makes it easier to achieve comprehensive and accurate responses with your RAG model.
Our team of experts can:
How Sama helps: We can validate the accuracy and quality of retrieved information, assess how well the model follows instructions and completes tasks, evaluate contextual relevance of responses, and rank preferred responses and include the reasons why.
Expected outcomes: You can expect more accurate and contextually relevant responses, improved customer experiences, fewer errors and hallucinations, more effective user personalization, additional datasets to retrain your model, and faster resolution of customer queries.
How Sama helps: We can validate the accuracy and quality of retrieved information specific to your domain, evaluate responses for alignment with user intent, rank preferred responses and include detailed reasoning, assess how well the model follows instructions, and evaluate the model’s ability to integrate multiple external sources.
Expected outcomes: You can expect more accurate, comprehensive responses, fewer errors and hallucinations, improved customer personalization, faster model performance, improved adherence to policy and compliance, and a better customer experience.
How Sama helps: We can validate responses for relevance and accuracy, rank preferred responses, assess the model’s ability to follow instructions, and evaluate responses for alignment and clarity using pre-defined criteria, while ensuring that outputs are policy compliant.
Expected outcomes: Proper evaluation enhances the accuracy of both retrieved information and generated responses, minimizes errors and corrects biases, ensures better adherence to policies and compliance standards, boosts user efficiency, streamlines routine tasks such as onboarding and training, and ultimately elevates the overall user experience.
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