RAG Performance Improvement Service

Unlock the Full Potential of RAG (Retrieval Augmented Generation) with Netwoven's Expert Services

Overview
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Master RAG Implementation for Real-World Success

RAG, while easy to prototype, poses significant challenges when it comes to production adoption. Basic implementations may achieve 80% of the objectives, but the remaining 20% is critical for seamless integration and adoption in real-world scenarios. At Netwoven, we specialize in bridging this gap, offering advanced RAG implementation services tailored to your specific needs.

Why Choose Netwoven?

Expertise in RAG Adoption: With years of experience in AI technologies, our team understands the intricacies of RAG adoption and is equipped to overcome the challenges associated with product ionization.

Tailored LLM Solutions: We provide customized solutions leveraging Large Language Models (LLMs) to ensure that your RAG implementations deliver maximum impact and value.

Services

Maximize the potential of RAG with Netwoven's expert services. Our comprehensive offerings ensure that your RAG implementations meet the highest standards of performance and quality. Contact us today to learn more about how we can elevate your AI initiatives with advanced RAG implementation.

Data Cleansing

Extract key insights from data and classify them through advanced visualization techniques, facilitating informed cleansing decisions. We leverage LLM-based abstractions to enhance data cleansing efficiency.

Selection of Optimal Index Types

Design use case-specific index structures for source content, utilizing multiple indexes in a hybrid setup to optimize performance and response quality.

Identify Preferred Chunking Process

Define chunking strategies tailored to your use cases, considering factors such as content nature, desired performance, and response expectations.

Base Prompt Designing

Define base prompts for different usage contexts and personas, enabling auto-generation of prompts from current usage context and user queries.

Metadata Filtering

Automatically generate metadata during indexing and tag chunks accordingly, prioritizing chunks based on metadata for enhanced response generation.

Query Routing

Design query routing plans to automatically route queries to different dedicated indexes, optimizing search efficiency and response accuracy.

Select Reranking Algorithm

Design optimal reranking strategies to resolve conflicts between similarity and relevance, ensuring the selection of the most important content for response generation.