M
Michael Marolda
Guest
It’s no secret that AI tools like ChatGPT have already reshaped the way we work. Large language models (LLMs) can summarize documents, brainstorm ideas and draft content in seconds. This makes enhanced productivity achievable within a few prompts.
At the same time, enterprises are struggling with a different challenge altogether: how to use AI across data environments. Generic outputs add little value unless agentic RAG is infused with an organization’s own data. Given regulatory requirements and sensitive data, like PII, context-rich AI becomes a tall order.
In this post, we’ll explore three different AI approaches, comparing how each performs, not just in generating answers, but in building reliable AI systems that deserve an organization’s trust. This goes beyond speed and agility to highlight the key value driver: providing accurate, useful information.
ChatGPT is an excellent tool for individual use. It helps people think through ideas, draft content, summarize information and answer questions quickly. For personal productivity and small-team workflows, it can be a powerful way to work faster and more creatively.
But businesses have very different needs. To make optimal use of AI, they require secure access to their own information and the ability to control who accesses it. AI answers should also reflect approved policies, documentation and regulatory requirements. This demands systems capable of managing knowledge over time, enforcing permissions and providing visibility into answer sources.
On its own, ChatGPT was not designed to meet these enterprise requirements. While effective for individual productivity, it doesn’t deliver the level of trust, transparency and accountability needed for enterprise knowledge systems. It isn’t a centralized organizational knowledge base with reliable links to answers generated from approved business content
ChatGPT is a large language model (LLM) designed for conversational interaction. Its algorithm is trained on vast quantities of data and relies on user-provided prompts and conversational context to inform its answers. Key use cases include research assistance, content creation and answering simple questions.
The main constraint of ChatGPT is its lack of customization for business purposes. Lacking an internal knowledge center, it has no built-in system for governance or permissioning. And without access to internal knowledge systems, it can’t tap into internal documents to add a contextual layer. This leaves answers generic and disconnected from the company’s precise business needs.
Retrieval-augmented generation (RAG) improves on standalone LLMs by generating responses using sources outside its training data. Instead, RAG pulls relevant paragraphs, sentences or timestamps from documents, audio and video files in a knowledge store. The language model then uses those documents to add context and generate more relevant answers.
This approach can dramatically reduce the occurrence of AI hallucinations, false answers presented as true by grounding every response in the RAG systems content and cited sources. That means answers are based on current, authoritative information, not generalized internet knowledge. The result is AI that understands your business context, speaks with your organization’s voice, and delivers answers that are not only accurate, but immediately actionable and trustworthy.
While RAG is a major step forward for organizations wanting to leverage their own data in AI systems, traditional RAG also presents challenges. RAG implementations can be complex and time-consuming to build, relying on static pipelines that require frequent manual tuning. Often time, LLMs are hardcoded into RAG pipelines making moving to the latest model a heavy engineering burden. Traceability to original source documents can prove to be a difficult task for small engineering teams. Governance and observability are limited with minimal feedback loops for continuous improvement, making it difficult to meet an organization’s needs for trust and long-term optimization.
The Progress Agentic RAG solution takes traditional RAG a step further by introducing intelligent agents. These agents search for information, make sense of it and then verify results before providing an answer. This rich capability elevates single-prompt LLMs into a scalable, powerful AI engine built for agility, flexibility, empowering efficient business operations.
The Progress Agentic RAG solution capabilities include:
With ChatGPT, support teams might receive different answers depending on how a specific question is phrased, and those answers may not use the most recent, approved reference information.
The Progress Agentic RAG solution answers are permission-based and generated using trusted internal knowledge bases, with clear references to source material. Support teams can act with confidence, knowing every answer aligns with approved policies and up-to-date product information.
Citations prove accuracy. ChatGPT isn’t designed to provide an audit trail for compliance-related questions and can’t reliably point back to an organization’s official sources or documents.
The Progress Agentic RAG solution answers cite sources and link directly to approved sections, paragraphs, or images in documents and timestamps in video/audio files, making it easier for organizations to meet regulatory requirements by knowing where the information came from.
When handling highly specialized technical questions, ChatGPT may rely on general training data and then make educated guesses. The Progress Agentic responses source from internal technical documentation, specifications and designated repositories. This empowers engineers with accurate, context-aware guidance they can trust.
For human resources teams, ChatGPT queries can create unnecessary risks if answers are outdated or incorrect. Progress Agentic RAG provides role-based access to current HR policies, so HR teams have reliable, controlled guidance aligned with the company’s rules and procedures.
To successfully integrate AI into an enterprise, organizations need more than fast answers. They need information they can trust. Confidence comes from transparency and control. With it, AI can cement itself into your organization as a core business capability vs. an unproven tech experiment.
ChatGPT largely functions as a black box. And while it can provide citations, they are inconsistent and aren’t designed to enforce organizational policies. This makes it difficult to rely on ChatGPT for regulated or mission-critical business processes.
The Progress Agentic RAG foundation has trust built into its design. Through modular pipelines, flexible deployment options and governance controls, every response can be traced back to its source documents and reviewed when needed. Outputs are continuously evaluated using REMi, helping to improve accuracy and reliability over time. By combining transparency, accountability and ongoing optimization, Progress Agentic RAG turns AI into a dependable enterprise knowledge system that organizations can use for critical business decisions.
As a modular RAG pipeline, the Progress Agentic RAG solution provides a reusable knowledge foundation that adapts as needs evolve. Organizations can swap retrieval strategies, models, embeddings and data sources without rebuilding their AI stack, ensuring the same governed knowledge layer can power AI search, assistants and future AI experiences. This flexibility allows teams to start with one use case and confidently expand, knowing the underlying knowledge system is built to scale, evolve and support enterprise AI for the long-term.
Enterprise AI is moving beyond experimentation and into meaningful everyday business use. The true value comes in helping teams access trusted information to make better decisions by using their own data.
Getting started with Progress Agentic RAG is intuitive and can be accomplished in minutes vs. the days or weeks required by other RAG solutions. Organizations often begin with a targeted use case, like customer support or internal documentation. By connecting reliable data sources and setting clear access controls, teams can introduce intelligent agents that retrieve and compose information in a structured and dependable way.
With its modular, scalable design, Progress Agentic RAG is designed to grow and morph to meet changing business requirements and new AI experience needs. New data sources and evaluation methods can then be added or revised to help organizations move from standalone AI tools to integrated knowledge systems that confidently scale and deliver immediate value.
Learn how Progress Agentic RAG can work for your organization. Book a demo or sign up for a free trial.
Continue reading...
At the same time, enterprises are struggling with a different challenge altogether: how to use AI across data environments. Generic outputs add little value unless agentic RAG is infused with an organization’s own data. Given regulatory requirements and sensitive data, like PII, context-rich AI becomes a tall order.
In this post, we’ll explore three different AI approaches, comparing how each performs, not just in generating answers, but in building reliable AI systems that deserve an organization’s trust. This goes beyond speed and agility to highlight the key value driver: providing accurate, useful information.
Individual Productivity vs. Enterprise Knowledge Layers
ChatGPT is an excellent tool for individual use. It helps people think through ideas, draft content, summarize information and answer questions quickly. For personal productivity and small-team workflows, it can be a powerful way to work faster and more creatively.
But businesses have very different needs. To make optimal use of AI, they require secure access to their own information and the ability to control who accesses it. AI answers should also reflect approved policies, documentation and regulatory requirements. This demands systems capable of managing knowledge over time, enforcing permissions and providing visibility into answer sources.
On its own, ChatGPT was not designed to meet these enterprise requirements. While effective for individual productivity, it doesn’t deliver the level of trust, transparency and accountability needed for enterprise knowledge systems. It isn’t a centralized organizational knowledge base with reliable links to answers generated from approved business content
ChatGPT, Traditional RAG and Progress Agentic RAG
ChatGPT: General-Purpose Generative AI
ChatGPT is a large language model (LLM) designed for conversational interaction. Its algorithm is trained on vast quantities of data and relies on user-provided prompts and conversational context to inform its answers. Key use cases include research assistance, content creation and answering simple questions.
The main constraint of ChatGPT is its lack of customization for business purposes. Lacking an internal knowledge center, it has no built-in system for governance or permissioning. And without access to internal knowledge systems, it can’t tap into internal documents to add a contextual layer. This leaves answers generic and disconnected from the company’s precise business needs.
Retrieval-Augmented Generation (RAG)
Retrieval-augmented generation (RAG) improves on standalone LLMs by generating responses using sources outside its training data. Instead, RAG pulls relevant paragraphs, sentences or timestamps from documents, audio and video files in a knowledge store. The language model then uses those documents to add context and generate more relevant answers.
This approach can dramatically reduce the occurrence of AI hallucinations, false answers presented as true by grounding every response in the RAG systems content and cited sources. That means answers are based on current, authoritative information, not generalized internet knowledge. The result is AI that understands your business context, speaks with your organization’s voice, and delivers answers that are not only accurate, but immediately actionable and trustworthy.
While RAG is a major step forward for organizations wanting to leverage their own data in AI systems, traditional RAG also presents challenges. RAG implementations can be complex and time-consuming to build, relying on static pipelines that require frequent manual tuning. Often time, LLMs are hardcoded into RAG pipelines making moving to the latest model a heavy engineering burden. Traceability to original source documents can prove to be a difficult task for small engineering teams. Governance and observability are limited with minimal feedback loops for continuous improvement, making it difficult to meet an organization’s needs for trust and long-term optimization.
Progress Agentic RAG
The Progress Agentic RAG solution takes traditional RAG a step further by introducing intelligent agents. These agents search for information, make sense of it and then verify results before providing an answer. This rich capability elevates single-prompt LLMs into a scalable, powerful AI engine built for agility, flexibility, empowering efficient business operations.
The Progress Agentic RAG solution capabilities include:
- Modular solution designed for many AI experiences
- 30+ tuneable retrieval strategies
- 40+ LLMs available with one-click model switching
- Fast, accurate and business relevant answers
- Dramatically reduced hallucinations
- Permission-aware data access
- Traceability to the original source information with citations
- Continuous feedback to improve answer quality
- RAG evaluation metrics (REMi) for optimization
Use Case Examples: ChatGPT vs. Progress Agentic RAG
1. Customer Support Knowledge Retrieval
With ChatGPT, support teams might receive different answers depending on how a specific question is phrased, and those answers may not use the most recent, approved reference information.
The Progress Agentic RAG solution answers are permission-based and generated using trusted internal knowledge bases, with clear references to source material. Support teams can act with confidence, knowing every answer aligns with approved policies and up-to-date product information.
2. Regulatory and Compliance Queries
Citations prove accuracy. ChatGPT isn’t designed to provide an audit trail for compliance-related questions and can’t reliably point back to an organization’s official sources or documents.
The Progress Agentic RAG solution answers cite sources and link directly to approved sections, paragraphs, or images in documents and timestamps in video/audio files, making it easier for organizations to meet regulatory requirements by knowing where the information came from.
3. Engineering and Technical Documentation
When handling highly specialized technical questions, ChatGPT may rely on general training data and then make educated guesses. The Progress Agentic responses source from internal technical documentation, specifications and designated repositories. This empowers engineers with accurate, context-aware guidance they can trust.
4. HR and Policy Guidance
For human resources teams, ChatGPT queries can create unnecessary risks if answers are outdated or incorrect. Progress Agentic RAG provides role-based access to current HR policies, so HR teams have reliable, controlled guidance aligned with the company’s rules and procedures.
Trust as the Foundation of Enterprise AI
To successfully integrate AI into an enterprise, organizations need more than fast answers. They need information they can trust. Confidence comes from transparency and control. With it, AI can cement itself into your organization as a core business capability vs. an unproven tech experiment.
ChatGPT largely functions as a black box. And while it can provide citations, they are inconsistent and aren’t designed to enforce organizational policies. This makes it difficult to rely on ChatGPT for regulated or mission-critical business processes.
The Progress Agentic RAG foundation has trust built into its design. Through modular pipelines, flexible deployment options and governance controls, every response can be traced back to its source documents and reviewed when needed. Outputs are continuously evaluated using REMi, helping to improve accuracy and reliability over time. By combining transparency, accountability and ongoing optimization, Progress Agentic RAG turns AI into a dependable enterprise knowledge system that organizations can use for critical business decisions.
As a modular RAG pipeline, the Progress Agentic RAG solution provides a reusable knowledge foundation that adapts as needs evolve. Organizations can swap retrieval strategies, models, embeddings and data sources without rebuilding their AI stack, ensuring the same governed knowledge layer can power AI search, assistants and future AI experiences. This flexibility allows teams to start with one use case and confidently expand, knowing the underlying knowledge system is built to scale, evolve and support enterprise AI for the long-term.
Final Thoughts
Enterprise AI is moving beyond experimentation and into meaningful everyday business use. The true value comes in helping teams access trusted information to make better decisions by using their own data.
Getting started with Progress Agentic RAG is intuitive and can be accomplished in minutes vs. the days or weeks required by other RAG solutions. Organizations often begin with a targeted use case, like customer support or internal documentation. By connecting reliable data sources and setting clear access controls, teams can introduce intelligent agents that retrieve and compose information in a structured and dependable way.
With its modular, scalable design, Progress Agentic RAG is designed to grow and morph to meet changing business requirements and new AI experience needs. New data sources and evaluation methods can then be added or revised to help organizations move from standalone AI tools to integrated knowledge systems that confidently scale and deliver immediate value.
Learn how Progress Agentic RAG can work for your organization. Book a demo or sign up for a free trial.
Continue reading...