As we step into 2025, Retrieval_-Augmented Generation (RAG) will further cement itself as a foundational component of AI workflows across industries. At its core, RAG combines the generative capabilities of large language models (LLMs) with access to external knowledge sources, enabling more accurate, reliable, and up-to-date AI outputs. 

However, the basic methods that defined RAG’s early applications—like simple vector-based document retrieval and query answering—are quickly becoming table stakes. To stay competitive, organizations must adopt more sophisticated techniques that deliver better experiences, deeper contextual understanding, and cost-efficient scalability.

This evolution demands innovation beyond the basics. Companies will need to explore advanced approaches to RAG, leveraging tools that allow for flexibility, experimentation, and integration with cutting-edge models. Techniques like contextual retrieval, multimodal integration, and reranking will play an increasingly critical role in delivering next-level accuracy and relevance.Parasail is leading this charge, making RAG not just accessible but transformative through advanced capabilities like open-source model support, cost-efficient batch processing, and enhanced scalability.

In this blog, we’ll start by revisiting the foundational components of RAG before diving into how Parasail is uniquely positioned to help organizations elevate their RAG systems to meet the demands of 2025 and beyond.

The Basics of RAG

RAG operates in three distinct stages:

1. Document Vectorization: Documents are divided into smaller chunks and converted into vectors using embedding models. These vectors are stored in a vector database for efficient retrieval.
2. Similarity-Based Retrieval: When a query is made, the system finds the most relevant document vectors using metrics like cosine similarity.
3. Context-Enhanced Generation: The retrieved content is combined with the original query, enabling the LLM to generate a more informed and accurate response.

This basic architecture empowers diverse applications, from retrieving product manuals to answering complex scientific queries. However, as the technology matures, organizations must move beyond this foundation to unlock the full potential of RAG.

Challenges with RAG Systems

While As RAG systems evolve, organizations are encountering several challenges that make advanced implementations essential:

• Cost Constraints and rate limits: Building and maintaining RAG systems (not to mention all the experimentation during prototyping) often cost a lot of money due to working with expensive inference providers (serving proprietary or open source models) or managing expensive GPU clusters directly.
• Inflexibility: Most inference providers (serving proprietary or open source models) restrict their users to a small set of models, limiting experimentation and customization for unique datasets or use cases.
• Security Concerns: There are various kinds of security and privacy concerns when using inference providers in RAG systems (e.g. vulnerabilities of proprietary models and attempts to resolve them may be less exposed to the public) 

Addressing these challenges requires a shift toward more flexible, cost-efficient, and scalable solutions. Parasail is uniquely positioned to meet these needs.

Parasail’s Approach to RAG

Parasail is tackling these challenges head-on with a platform designed to support advanced RAG workflows in 2025 and beyond:

1. Support for Open-Source Models:

Open-source models are advancing rapidly, offering performance on par with or exceeding proprietary options. Parasail enables organizations to:

• Experiment with state-of-the-art open-source models like parasail-ai/GritLM-7B-vllm (from Contextual AI) and Alibaba-NLP/gte-Qwen2-7B.
• Integrate their own custom or private models for unique use cases.
• Evaluate models on specific datasets to optimize for accuracy, cost, speed, and storage.

Why this matters: Open-source models not only lower costs but also offer transparency and adaptability. By eliminating reliance on third party providers, businesses can fine-tune workflows to their needs and deploy safely in an environment that is completely in their control.

Our experiments have shown that different models shine on different datasets. Thus it's useful to have the ability to experiment with a variety of models to find the best one for the use case at hand while making trade-offs between quality, cost, speed, and storage. For a detailed breakdown of our model evaluation process and experiments, check out our notebook here.

For example, the open-source parasail-ai/GritLM-7B-vllm (adapted from Contextual’s GritLM) and Alibaba-NLP/gte-Qwen2-7B-instruct have demonstrated performance parity with proprietary models like Voyage AI’s voyage-3 and OpenAI’s text-embedding-3-large in a SKILL-Language dataset evaluation. In future blog posts, we’ll highlight open source models for the real-time answer generation portion of a RAG engine that are on par with proprietary OpenAI and Anthropic models. 

2. Batch Processing and Cost Savings:

A key differentiator of the Parasail platform is support for batch processing of most open source transformers on HuggingFace. This enables organizations to handle large-scale analytical workloads efficiently, especially the preprocessing and embeddings that are crucial to RAG engines. 

• Batch Processing Makes Data Processing Effortless:  Batch processing allows large jobs – millions or even billions of prompts – to be remotely queued and processed automatically at maximum utilization.  Developers can go to sleep and wake up knowing the job will be done. 
• Parasail’s Cost Savings: Parasail offers a 30-50% discount on batch processing depending on our server, on top of our existing 2-4x savings over open-source model providers and 10-30x savings over proprietary providers.
• Developer Friendly: Seamless integration via OpenAI-compatible Batch APIs, simplifying the adoption process. Use our OpenAI batch helper library to make things even easier. 

Why this matters: Batch processing is essential for handling large-scale analytical workloads efficiently and these savings enable organizations to scale their RAG systems affordably whether it’s indexing millions of documents or vectorizing video data for RAG workflows.

The following compares the cost of running GritLM/GritLM-7B on the Parasail platform with the cost of proprietary models on 50 billion tokens.

3. Real-Time Performance and Scale

Parasail ensures that real-time components of Retrieval-Augmented Generation (RAG)—the step where retrieved chunks and queries are processed to generate immediate answers—are handled with exceptional speed, scalability, and cost efficiency.

This stage of RAG is highly sensitive to latency and cost, as users expect rapid responses without compromising accuracy. Parasail addresses these needs through advanced infrastructure, including cutting-edge GPUs and hybrid Kubernetes clusters, enabling models to process tokens faster and respond to user queries with minimal delay. 

By offering a variety of high-performance models, like parasail-ai/GritLM-7B-vllm and Alibaba-NLP/gte-Qwen2-7B-instruct, alongside optimized orchestration for real-time workloads, Parasail supports use cases where instantaneous retrieval and generation are critical.

Why this matters: Faster real-time processing enables organizations to meet rising user expectations, iterate quickly, and deliver seamless AI-driven experiences without the burdens of high costs or complex infrastructure management.

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RAG in Action: Use Cases

As organizations move beyond basic RAG implementations, Parasail enables them to unlock new possibilities across a range of industries:

Scientific Document Retrieval

Researchers can retrieve relevant studies, technical papers, or datasets with pinpoint accuracy, dramatically reducing time spent on manual searches. Parasail’s cost-effective and scalable platform empowers these workflows without breaking the bank.

Multimedia Indexing and Retrieval

Organizations with large volumes of multimedia content, such as video or image libraries, can use Parasail’s RAG capabilities to efficiently index and retrieve specific assets. Applications range from video search in entertainment to cataloging footage for compliance or training purposes.

Enhanced Technical Support

Companies use RAG to dynamically retrieve product manuals, troubleshooting guides, or FAQs, enabling faster and more accurate customer support.

Marketing and Content Insights

Multimodal RAG capabilities allow marketers to analyze image-rich social media content and campaign performance, uncovering actionable insights that drive engagement and ROI.

Why 2025 Is the Year of RAG

Basic RAG workflows are no longer enough. As organizations grapple with rising user expectations and increasingly complex data environments, the need for more sophisticated RAG techniques has never been greater. The increasing power of open-source LLMs, combined with the falling costs of compute, enables organizations to derive far more powerful insights from their data.

2025 will separate the leaders from the laggards in AI innovation. Organizations that embrace advanced RAG methods—such as contextual retrieval, multimodal integration, and batch processing—will be better equipped to deliver transformative experiences and achieve operational excellence. Future blog posts will dive deeper into these advanced techniques, exploring how innovations like contextual RAG can significantly improve relevance, accuracy, and cost-efficiency across diverse applications.

Redefining RAG for 2025 with Parasail

Parasail is empowering organizations to push the boundaries of what’s possible with RAG. By combining open-source innovation, cost-efficient batch processing, and cutting-edge performance, we’re helping businesses move beyond basic implementations and embrace the future of RAG.

Ready to make RAG a competitive advantage in 2025? Contact Parasail today to learn how we can help you unlock actionable insights and scalable AI workflows—at a fraction of the cost.