RETRIEVAL-AUGMENTED FACTUALITY
mprove AI accuracy with context-sensitive validation, testing retrieval-augmented systems to ensure reliable, fact-based outputs.
Content
Project Description
AI/LLM
USA
2025
3 QA engineers
Overview
This case study highlights how Tesvan implemented retrieval-augmented factuality checks using context-sensitivity techniques to power a modern AI chatbot. The solution leverages large language models (LLMs) and embedding-based retrieval to provide contextually relevant, human-like, and factually accurate responses.
By ingesting website content and internal documentation, the chatbot processes domain-specific data into embeddings stored in a vector database. Through a Retrieval-Augmented Generation (RAG) approach, the system grounds answers in verified information, minimizing hallucinations and improving trustworthiness.
A state machine manages conversation flow, intent classification, and tool integrations (CRM, scheduling, and follow-up). Built on a scalable API framework, the chatbot delivers seamless interactions across both CLI and web interfaces with rapid response times.
Objective
- Guarantee factual accuracy through retrieval-augmented factuality checks.
- Maintain context sensitivity in multi-turn dialogues.
- Automate lead qualification and follow-up with tool integrations.
- Ensure scalable, low-latency performance across interfaces.
Challenge
- Preventing LLM hallucinations and fabricated outputs.
- Preserving contextual consistency in complex conversations.
- Managing integration with multiple enterprise tools.
- Achieving fast responses while retrieving from vector databases.
Solution
- Built a RAG pipeline to connect chatbot answers with source-of-truth data.
- Applied retrieval-augmented factuality checks using context-sensitivity techniques at every response step.
- Deployed a state machine for coherent, context-sensitive dialogue.
- Integrated CRM and scheduling systems for automation.
- Delivered on a scalable API framework supporting both CLI and web use.
Result
By implementing retrieval-augmented factuality checks using context-sensitivity techniques, Tesvan achieved significant performance and business gains:
98%
factual accuracy
40%
fewer hallucinations
65%
faster lead qualification
2,5x
higher engagement
30%
cost savings
Other Cases
AI/LLM
Layered AI Testing
Validate every dimension of your AI system: functionality...
AI/LLM
Hallucination Testing in Chatbots
Hallucination Testing in Chatbots...
Management
Clustercontrol
Tesvan enhanced ClusterControl’s QA by creating structure...
Do you want to discuss your project?
Testimonials
Aleksey Kudrya
Founder, Mnemonic Words
Janell Pechacek
tailwindapp.com
Raymond Huang
Cofounder, legalatoms.com
Aleksey Kudrya
Founder, Mnemonic Words