Improving model accuracy with grounded data

Understanding the Challenge

A key challenge with foundation models (FM) is their tendency to generate “hallucinations” — outputs that may be incorrect, fabricated, or nonsensical, especially when answering open-ended questions. These hallucinations occur because FMs rely solely on their training data, which may be incomplete or biased, and they lack an internal mechanism to distinguish accurate information from plausible but false content.

To reduce such hallucinations, the grounding method can be applied. It involves integrating FMs with an information retrieval system that searches external databases or document collections to find relevant and reliable information during the model’s inference process. The retrieved data is then fed back into the model as additional input, ensuring that its responses are guided by verified real-world information.

This technique, known as Retrieval-Augmented Generation (RAG), allows FMs to produce outputs aligned with external data sources, thereby improving factual accuracy and reducing the risk of hallucinations.

Our AI consulting service specializes in improving the accuracy of generative AI solutions by integrating retrieval-augmented generation methods, ensuring that your models deliver factually correct, reliable, and up-to-date responses for critical business applications.

Retrieval-Augmented Generation (RAG) for improved accuracy

• Custom RAG architectures: We design and implement RAG architectures using FAISS, Pinecone, and Weaviate for high-speed vector retrieval.
• Semantic search and dense embeddings: We implement Sentence-BERT, OpenAI embeddings, and ColBERT to enhance contextual relevance when retrieving external knowledge.
• Hybrid search optimization: We combine keyword-based retrieval (BM25) with vector search for higher precision.

Fine-tuning and adaptive model optimization

• Parameter-efficient fine-tuning (PEFT): We apply techniques such as LoRA, Adapters, and BitFit to train models with reliable proprietary data at minimal computational cost.
• Integration of knowledge from graphs and structured data: We integrate Neo4j and RDF ontologies to enable logical reasoning over structured knowledge.

Latency optimization and scalable deployment

• Low-latency queries: We implement in-memory caching (Redis, Memcached) and request preprocessing to reduce response times under heavy load.
• Real-time knowledge retrieval: We enable integration with real-time APIs (e.g., financial, legal, or healthcare databases) to keep AI outputs dynamically updated.

Enterprise security and compliance

• Data governance and traceability: We ensure role-based access control (RBAC) and compliance with GDPR, HIPAA, and SOC2 for responsible AI use.
• Automated fact-checking pipelines: We implement AI-based output verification using secondary models that cross-check the reliability of answers before presenting them to users.

✅ Increased trust and accuracy by eliminating AI hallucinations.
✅ Improved decision-making processes by ensuring factual grounding of AI-generated insights.
✅ Optimized AI performance by balancing retrieval efficiency, inference latency, and response quality.
✅ Ensured compliance and transparency by reducing the risk of misinformation in sensitive industries.

💡 Partner with us to implement AI systems that are not only intelligent but also reliable, scalable, and enterprise-ready.

Client: Leading online health information platform Solution: We collaborated with a leading online health information platform to improve the reliability of their AI medical advice chatbot. The platform faced challenges in ensuring the accuracy of answers, particularly when providing trusted health information to users.

By applying grounding techniques and integrating the chatbot with reliable medical databases and peer-reviewed journals, we significantly improved the factual accuracy of the responses. Using retrieval-augmented generation (RAG), the model could now reference up-to-date and trustworthy medical information, reducing hallucinations and providing users with evidence-based advice.

Text representation: gauge: 30%
Description: By integrating grounding techniques into our healthcare AI chatbot, we increased user trust and engagement, boosting daily active users by 30% and session length by 40%.