Retrieval-Augmented Generation (RAG)
Transform AI with Teneo RAG
Unlock instant, accurate answers with Teneo RAG. Simply upload your knowledge, and let Teneo’s smart AI turn it into a powerful resource for customer and agent interactions.
In seconds, your bot is ready to respond to questions with precision, enhancing experiences and making information accessible like never before.
Teneo RAG in Action
See how Teneo’s AI agents revolutionize airline customer service by delivering highly accurate, responsive support.
6 Challenges with RAG
While RAG offers promising AI capabilities, transitioning from a proof of concept (POC) to a live environment presents challenges:
Monitoring and Control
Effective monitoring is essential to manage RAG outputs, a core challenge in achieving consistent quality with agentic precision.
Limited Visibility and User Feedback Integration
The lack the capability to fully see and understand user interactions, hindering the ability to control responses and integrate user feedback effectively.
Maintaining Relevance and Accuracy
Continuous updates and maintenance are necessary for RAG to stay relevant and accurate, a task that grows increasingly complex in live environments.
Complex Integration
Integrating RAG into existing systems comes with high cost of technical labour and often becomes a hurdle in moving from POC to full-scale deployment.
Scalability Issues
Scaling RAG to handle real-world data and diverse interactions is challenging; Teneo’s AI agents are optimized for reliable, large-scale performance.
Performance Optimization
Ensuring RAG operates with optimal speed and accuracy across various business scenarios is a significant challenge during the scaling process.
Enhance Your AI Agents with Teneo Copilot
Teneo Copilot empowers your AI agents with Agentic AI by enabling seamless integration with any LLM, allowing you to effortlessly generate entries and responses. With a user-friendly interface, Copilot accelerates your workflow and enhances development capabilities, making it easy to keep your AI agents up-to-date and responsive.
Generate Classes
Generate Responses
Create Entities
Add Your Own LLM
Why Teneo for RAG
With Teneo RAG your LLM will be accurate, effective and cost optimized.
Discover how Teneo makes RAG more efficient, reliable, and insightful for your business needs:
98% Cost Reduction
Teneo´s RAG is based on FrugalGPT and increase accuracy with prompt tuning.
Monitor RAG Behavior
Teneo's monitoring tools allow businesses to understand and validate RAG's responses, ensuring AI interactions are aligned with business goals.
Control AI Responses
Adjust and refine RAG's outputs with Teneo's control features, ensuring accuracy and relevance in every interaction. Teneo cover the areas that RAG misses.
Listening to User Interactions
Teneo analyzes user interactions with RAG, providing insights to tailor AI responses to your audience's needs. Teneo defines which flow to call, extract info and orchestrates.
Benefits of Teneo RAG
Teneo enhances RAG's efficiency, ensuring faster and more effective AI operations.
Gain deep insights into user preferences and behavior with Teneo's advanced analytics.
Customize RAG's functionalities to suit specific business requirements and scenarios with Teneo's flexible tools.
Frequently Asked Questions
What is Retrieval-Augmented Generation (RAG)?
RAG is an AI approach that combines a retrieval component (searching a knowledge base) with a generative language model (like OpenAI GPT-4o and Anthropic Claude). When a user asks a question, the system first finds relevant documents or data (“retrieval”) and then conditions the LLM on that retrieved context to generate accurate, up-to-date answers.
How does RAG work in practice?
It consists of three parts, 1. Indexing your corpus (documents, FAQs, manuals) is embedded and stored in a search index. 2. Retrieval, at query time, relevant passages are fetched via similarity search. 3. Augmented Generation. The LLM ingests those passages alongside the user’s prompt, yielding answers grounded in your own data rather than just its pretrained weights.
What are the key benefits of RAG?
Accuracy & Relevance: Answers directly reference your source material. Up-to-Date Knowledge: You control updates, no reliance on a model’s training cutoff. Cost Efficiency: By narrowing the generation scope to retrieved snippets, you often reduce token usage. Customizability: You choose what content the AI Agent can “see,” ensuring domain alignment.
What challenges commonly arise when taking RAG from proof-of-concept to production?
1. Monitoring & Control: Ensuring the LLM doesn’t “hallucinate” or drift off-brand.
2. Visibility & Feedback: Gaining insights into which docs get used and how users interact.
3. Relevance & Maintenance: Keeping the indexed content fresh as your knowledge evolves.
4. Integration Overhead: Connecting retrieval, LLM, and your existing systems reliably.
5. Scalability: Serving high volumes of queries with consistent latency.
6. Performance Tuning: Balancing speed vs. answer quality at scale.
How does Teneo enhance a RAG deployment?
Teneo enhances RAG deployments in following ways:
1. Leveraing Stanford University’s FrugalGPT & Prompt Tuning: Dramatically reduce AI costs (up to 98 %) while boosting answer precision.
2. Monitoring Suite: access to analytics to expose retrieval behavior, confidence metrics, and fallback rates.
3. Control Features: Fine-grained filters and override rules ensure consistency with your brand and compliance requirements.
4. Orchestration Layer: Seamlessly wire retrieval, LLM, and business-rule engines into your existing channels and systems.
How do I get started with RAG using Teneo?
You can start and deploy a fully functional RAG AI Agent with Teneo in just three simple steps—and have it live in minutes—by using our native Generative QnA template solution. Contact us to learn more
Get Started with RAG
Kickstart your Retrieval-Augmented Generation (RAG) journey using Teneo’s built-in RAG Agent template
Microsoft Azure
By using Teneo with Microsoft Azure, you can:
Integrate with Azure AI Search for powerful semantic search capabilities
Utilize Azure OpenAI Service to access large language models
Store and manage data via Azure Storage Accounts
Amazon Web Services
By using Teneo with AWS, you can:
Employ Amazon OpenSearch for efficient data indexing and search
Use Amazon Bedrock to access foundation models such as Anthropic Claude
Manage files and data through AWS S3