When people have questions, they want answers – not piles of documents. Today, the expectation for instant, accurate, and relevant responses is higher than ever. Generative AI has revolutionized how we access information, but much of its power remains out of reach for businesses due to concerns around accuracy and security. This is where retrieval-augmented generation (RAG) comes in.

RAG combines the capabilities of large language models (LLMs) with trusted internal data to deliver accurate, relevant, and grounded answers. This approach minimizes risks like hallucination and misinformation and enhances decision-making by providing reliable insights.

But for RAG to be truly effective, it needs a strong foundation, and that foundation starts with proper ingestion.

Ingestion is the backbone of your RAG pipeline. When done right, it brings together data from trusted sources, ensures it’s clean and normalized, and transforms it into an "AI-ready" format for LLMs to use efficiently. But when done wrong? You risk facing the "garbage in, garbage out" problem—where poor-quality data leads to subpar or even disastrous results.

In this article, we’ll guide AI and ML engineers through key considerations when building a custom RAG ingestion pipeline and explain how leveraging an out-of-the-box enterprise RAG solution like Pryon RAG Suite can help streamline the process and boost performance.

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1. Data sources: Where will your knowledge come from?

Your RAG pipeline is only as insightful as the data it can access. With enterprises using an average of 112 SaaS applications to store and manage content, unifying this data into a single system is a complex but critical step.

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Here’s what you need to ask:
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• Which data types will you leverage? Will you pull from structured databases, unstructured documents, web scraping, or internal knowledge bases?
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• ‍How will you manage static vs. dynamic content? How will you handle data that requires frequent updates or changes?
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• ‍How will you manage content from multiple sources simultaneously? Different systems of record (SOR) have varying methods for storing and representing metadata. How will you unify this disparate data to provide consistent access to the knowledge your RAG application needs?
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• ‍How will you ensure the quality and relevance of the data? Will you focus on high-quality, domain-specific sources, or aggregate a broader range of general knowledge? And what happens if you need to include both types to support the same use case?
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• ‍What’s your plan for data scale? How will your pipeline handle large, dynamic datasets that require frequent updates and real-time processing?
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• ‍How will you manage document access control and permissions? Are there sensitive documents or regulatory compliance requirements (e.g., GDPR, HIPAA) that require specific access control rules? How will you ensure that only authorized users and systems can retrieve or interact with specific pieces of data?

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The average enterprise uses 112 SaaS applications to store and manage content.

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Out-of-the-box advantage

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• Data source integration: Pre-built connectors integrate directly with diverse data sources—structured or unstructured—eliminating the need for custom integrations.
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• Pre-quality-checked data: Built-in mechanisms curate, clean, and validate data from multiple sources, ensuring reliability and relevance while reducing the need for manual data curation.
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• Scalable data handling: Optimized infrastructure can scale as data grows, seamlessly handling data updates, versioning, and maintenance.
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• Built-in access control: Integrated access control levels (ACL) ensure content is only visible to authorized users, with robust permission management features at a document, dataset, or user level.
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• Compliance-ready: Many enterprise RAG solutions, like Pryon, offer built-in compliance features, including audit logs, secure authentication, and data encryption, to meet regulatory standards such as GDPR, HIPAA, and SOC 2.
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By starting with a scalable, efficient integration process, you ensure your RAG system is grounded in accurate, comprehensive knowledge from day one.

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2. Data preprocessing: How will you prepare your data for retrieval?

Even the richest data can be unusable without proper preprocessing —a series of steps to clean, normalize, and structure the data so that it can be easily leveraged by your downstream LLM. From scanned documents to handwritten text, preprocessing ensures diverse inputs are normalized into actionable, AI-ready data for downstream AI processes.

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Here’s what you need to ask:

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• How will you handle unstructured data such as images, scanned documents, or handwritten text? What Optical Character Recognition (OCR), Handwritten Text Recognition (HTR), Text Segmentation and Recognition (TSR), or computer vision techniques will you use to extract relevant information from visual or scanned data? How will you integrate your parsers into the rest of your ingestion pipeline?
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• What preprocessing steps are necessary to clean and normalize the data? Will you need tokenization, lemmatization, or entity recognition?
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• How will you extract and structure relevant metadata? Will you capture document-level metadata like authorship, creation date, and document type to improve the quality of retrieval?  Will you use LLMs to create metadata about your data?
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• What strategies will you use to handle noisy or redundant data? How will you filter out irrelevant or duplicate content that could degrade retrieval quality and increase storage/embedding costs?

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Out-of-the-box advantage

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• OCR and computer vision integration: Built-in OCR and computer vision capabilities enable automatic extraction of machine-readable text from images, scanned documents, and PDFs, saving time and effort.
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• Pre-built NLP pipelines: Integrated data preprocessing pipelines—including text cleaning, tokenization, and entity recognition—save developers from building these components from scratch.
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• Automated metadata extraction: Automatically extract and organize metadata to enhance retrieval with relevant context.
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• Noise reduction: Advanced noise-filtering mechanisms (e.g., duplicate detection, semantic deduplication) ensure only relevant data is ingested, improving retrieval quality.
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Smart preprocessing ensures your data is not just ready, but optimized for retrieval, laying the groundwork for accurate responses from your RAG system.

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3. Indexing: How will you structure your data for efficient retrieval?  

Indexing is vital for making your data accessible in a split second. A well-structured index ensures both speed and scalability as your data grows.

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Here’s what you need to ask:

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• How will you index your data for fast and efficient retrieval? How will you extract your embeddings? Will you use traditional inverted indexes, vector embeddings, or a hybrid indexing approach?
  
  How will you chunk your documents? What chunk size will you use to effectively balance accuracy with latency with cost?  Where will you store your embeddings and chunks?
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• What embedding strategies will you adopt? Will you use pre-trained embeddings, like sentence transformers, or fine-tune embeddings for your specific domain?
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• How will you ensure your indexing solution can scale? Will your chosen indexing framework be able to handle growing datasets efficiently?
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• How will you manage data updates and versioning in your index? Will your indexing solution support real-time updates, and how will you manage versioning to ensure your data is current and reliable?

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Out-of-the-box advantage

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• Optimized indexing systems: Highly optimized indexing systems handle sparse and dense retrieval out-of-the-box. These systems are designed for efficiency, scalability, and real-time performance.
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• Pre-configured embeddings: Pre-trained embeddings allow for easy fine-tuning with your domain-specific data, saving time on manual embedding and training steps.
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• Scalable, real-time indexing: Automatic handling of real-time data updates, versioning, and large-scale indexing means you won’t have to manage scaling infrastructure manually.
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By investing in a robust indexing process, you’ll give your RAG application the agility needed for enterprise-grade performance.
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4. Integration with the retrieval engine: How will your RAG system retrieve information to generate responses?  

A reliable retrieval engine bridges ingestion and real-time information delivery. Ensuring synchronization between these components avoids costly errors and ensures consistency.

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Here’s what you need to ask:

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• How will you ensure your data remains synchronized between the ingestion and retrieval pipelines? Will you implement real-time indexing, batch processing, or queueing mechanisms?
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• How will you maintain data consistency between the ingestion pipeline and the retrieval engine? What indexing strategy will you use to handle updates, deletions, or additions?
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• How will you manage errors and ensure fault tolerance? How will you deal with failures, such as ingestion lags or retrieval issues?  What error-handling mechanisms will you deploy if your ingestion pipeline fails or lags behind?  

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Out-of-the-box advantage

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• Seamless data synchronization: Optimized, real-time indexing and automatic updates ensure newly ingested data is immediately available for retrieval without manual intervention. This eliminates the need to worry about synchronization between ingestion and retrieval engines.
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• Built-in scalability and performance: Enterprise RAG systems like Pryon are optimized for high performance, capable of handling large volumes of data with speed and accuracy at scale.
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• Robust error handling and fault tolerance: Advanced error-handling mechanisms, like automatic retries, failover strategies, and alerting systems minimize disruptions caused by ingestion lags or retrieval failures, ensuring reliability of your RAG system.
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A seamlessly integrated retrieval engine ensures your RAG platform delivers precise, real-time answers every time.

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5. Cross use case applicability: How flexible is your RAG system for different use cases?

Enterprise needs aren’t static. A truly capable RAG system should scale easily across different use cases—from HR to compliance to customer service—without constant redevelopment.

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Here’s what you need to ask:

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• What effort is required for multi-use case scalability? How will you re-architect your RAG ingestion pipeline to handle different data types, context, or domains?
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• What is the time and effort required to adapt your RAG system to a different use case? What is the impact of needing to tweak data preparation and integration with retrieval engines when switching between use cases?

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Out-of-the-box advantage

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• Adaptability: Enterprise RAG tools like Pryon are designed with multi-use case scalability in mind. They come with the flexibility to handle various domains and data types out of the box, with pre-configured models that can be easily adapted to different use cases.
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• Time-to-value: Pre-configured pipelines and models support rapid deployment for various use cases. Pryon offers out-of-the-box flexibility, so you can quickly repurpose your RAG solution for a new task, while reducing time and development effort.
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By opting for a flexible system, you future-proof your RAG application against evolving business needs.

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Say goodbye to garbage data with an out-of-the-box enterprise RAG solution

Building a RAG pipeline can open the door to transformative AI applications while reducing the risks associated with ungrounded large language models. For highly specialized requirements, custom ingestion workflows may be necessary—but it's crucial to weigh the benefits of customization against the time, cost, and complexity of building from scratch.

Choosing an out-of-the-box enterprise RAG solution allows you to focus on innovation while ensuring your models are grounded in reliable data that drives meaningful results.

Let an out-of-the-box solution handle the complexities, so you can innovate faster and deliver smarter, more impactful AI applications.

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Dive deeper into enterprise RAG

Download Pryon’s Comprehensive Guide to Enterprise RAG and gain deep, actionable insights to overcome common implementation challenges.

Have questions? Reach out to our sales team to learn how Pryon’s powerful ingestion, retrieval, and generative capabilities can help you build and scale your enterprise RAG application in 2-6 weeks.

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