RETRIEVAL-AUGMENTED FACTUALITY
Improve 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:
40%
fewer hallucinations
65%
faster lead qualification
2,5x
higher engagement
30%
cost savings
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Testimonials
Aleksey Kudrya
Founder, Mnemonic Words
Janell Pechacek
tailwindapp.com
Raymond Huang
Cofounder, legalatoms.com
Aleksey Kudrya
Founder, Mnemonic Words