Retrieval Augmented Generation (RAG)
In the world of ChatGPT and other chatbots, fact-checking has become very important. It ascertains that the knowledge shared by AI is accurate and not based on the machine’s whims. That’s why you require Retrieval Augmented Generation (RAG) to boost large language models (LLMs). At CognoVerse, we do that by integrating real-time, authoritative external data sources available.
With this approach, we can improve the accuracy and relevance of AI-generated responses. Also, these responses become highly trustworthy thanks to RAG. RAG empowers your business with 100% updated, fully context-aware apps that are fact-based, not delusional or whimsical.
Are you ready to merge your AI’s intelligence with CognoVerse’s RAG solutions?
SERVICES
- Model Training
- Data Labeling
- Algorithm Design
- Model Optimization
- NLP Solutions
- Model Training
- Data Labeling
- Algorithm Design
- Model Optimization
- NLP Solutions
- Model Training
- Data Labeling
- Algorithm Design
- Model Optimization
- NLP Solutions
- Model Training
- Data Labeling
- Algorithm Design
- Model Optimization
- NLP Solutions
- Model Training
- Data Labeling
- Algorithm Design
- Model Optimization
- NLP Solutions
- Model Training
- Data Labeling
- Algorithm Design
- Model Optimization
- NLP Solutions
Integrating external knowledge bases to supplement LLM training data
Overcoming AI hallucinations by grounding responses in verified information
Offering real-time retrieval of relevant docs for precise and context-aware answers
Cutting costs & time by updating knowledge bases without retraining your LLMs
Why Choose us for Retrieval Augmented Generation (RAG) Development?
We can see that traditional LLMs give you a response based merely on their pre-trained data (remember when ChatGPT’s information was limited to the events of September 2021?). These responses can feel & sound outdated, even outright inaccurate. That’s why we at CognoVerse bridge this gap by combining powerful generative AI with dynamic information retrieval. This hybrid approach makes sure that your AI apps are delivering factually correct, verifiable outputs.
We at CognoVerse empower your business with AI solutions. Check out our comprehensive suite of AI services.
01
Curating External Data Sources
Our RAG service includes the ingestion of data sources like databases, document repositories, APIs, etc., all into vector databases. This is done using our embedding models that turn text into numerical representations.
02
Intelligent Retrieval Mechanisms
We also implement retrieval mechanisms that match user queries with relevant data sources. The retrieved documents are then compared to the user query. It’s done to augment the input for the LLM. That’s how the user gets a factually correct answer.
03
Automated Update Pipelines
Also, we at CognoVerse establish automated pipelines to keep your knowledge base current & accurate at all times. Also, we do monitoring, optimizing all processes. That’s how the RAG system we develop will evolve with your business needs.
01
Data Landscape Familiarity
At CognoVerse, we start by understanding your domain-specific knowledge requirements and data landscape to design a tailored RAG architecture that maximizes relevance and accuracy.
02
Flexible Data Ingestion
We can build modular and scalable systems. These systems integrate with your existing AI seamlessly. So, your AI infrastructure supports flexible data ingestion and retrieval.
03
Industry Standard Compliance
Ethical AI principles guide our development, emphasizing transparency, data privacy, and compliance with industry standards to build trust in AI-generated content.
04
Providing Iterative Refinement
We also work closely with your teams. They get the necessary training and support from us. Also, we give them iterative refinement to make sure that your RAG apps are improving all the time.
Testimonial
