Retrieval-Augmented Generation
Retrieval-Augmented Generation (RAG) is a powerful technique that extends the capabilities of Large Language Models by combining them with a custom knowledge base. Instead of relying solely on the model’s internal training data, RAG injects fresh, relevant information at runtime—making responses more accurate, traceable, and up-to-date.
At Dekkode, we support document ingestion, database integration, and dynamic data access through interfaces like function calling. Data is chunked into meaningful units, embedded using vector representations, and stored for retrieval. Depending on scale and performance needs, we use dedicated vector databases like Pinecone, Weaviate, or open-source solutions like FAISS when required and stick with lightweight solutions if suitable.
RAG brings LLMs closer to your business reality—enabling grounded, context-rich outputs that respect your source of truth.
Connect LLMs to Your Knowledge
Connect LLMs to Your Knowledge
RAG allows models to answer based on your data—not just what they were trained on. Think internal wikis, product manuals, or support logs.
Smart Chunking & Embeddings
We segment content into digestible, semantic chunks and convert them into high-dimensional vectors using advanced embedding models.
Scalable Vector Storage
From lightweight local stores to enterprise-grade vector databases, we tailor the solution to your needs—balancing speed, cost, and precision.
Real-Time Function Calling
Enhance retrieval with live database queries, API responses, or structured outputs by pairing RAG with function-calling capabilities.
Seamless Document Upload & Management
We enable fast onboarding of documents—PDFs, spreadsheets, and more—automatically indexed and ready for LLM-powered search and conversation.
RAG Use Cases
With Retrieval-Augmented Generation, you extend a LLMs knowledge with custom information. This is required in cases, where your data was not available when training the language model or you need access to real-time information.
Internal Knowledge Assistants
Answer employee questions using company documentation, onboarding manuals, HR guidelines, or IT support docs—without retraining an LLM.
Document Q&A and Search
Upload PDFs, reports, or legal contracts and enable users to ask questions in natural language, with references to the exact document sections.
Support Chatbots with Context
Enhance customer service bots with access to product guides, FAQs, and troubleshooting articles—ensuring accurate, context-aware responses.
Personalized Customer Interactions
Pull in relevant user data or historical interactions to personalize answers—powered by retrieval from CRM or database records.
Regulatory & Compliance Queries
Allow legal or compliance teams to query policy documents, regulations, and internal guidelines through a natural language interface.
Technical Documentation Assistant
Develop a dev-facing assistant that can answer technical questions by retrieving and summarizing content from API docs, codebases, or tickets.
Multilingual Content Retrieval
Support global teams by retrieving and generating localized answers from a multilingual document corpus.
Where you need RAG
- Internal Knowledge Assistants: Answer employee questions using company documentation, onboarding manuals, HR guidelines, or IT support docs—without retraining an LLM.
- Document Q&A and Search: Upload PDFs, reports, or legal contracts and enable users to ask questions in natural language, with references to the exact document sections.
- Support Chatbots with Context: Enhance customer service bots with access to product guides, FAQs, and troubleshooting articles—ensuring accurate, context-aware responses.
