Retrieval-augmented generation (RAG) has three main stages: retrieval, reasoning and generation. Retrieval finds the information in your enterprise knowledge base. The reasoning stage is responsible for interpreting, organizing and connecting the retrieved information so an answer can be found. The final step—generation—converts the reasoning into a logical, structured natural language-based response.

Only the first stage—retrieval—is the reality of what the model is allowed to reason over. Everything after retrieval is constrained by this initial step. Retrieval creates a temporary reality for the large language model (LLM). When you ask RAG a question, the retrieval step selects a small subset of information from your knowledge base and connected systems, which can compromise documents, policies, contracts and other business-related data. This information becomes the model’s entire source of truth for that specific interaction.

It is because of retrieval that RAG can provide value to enterprises leveraging AI by saying “no” to a specific question. If a fact is not retrieved using RAG, then it does not exist. If context is missing, then it cannot be inferred by the system. The model cannot try again because it cannot browse your systems and it cannot challenge the retrieval. The generation step can only leverage the retrieved information to provide an answer to a question.

How Retrieval Strategies Solve Enterprise Pain Points

Hallucinations: General-Purpose LLMs Gone Wrong

Retrieval fixes one of the largest challenges with generative AI today: hallucinations. When a user asks a general-purpose LLM a question, the LLM has two options: recall something from its training data or fill in the gaps with statistically plausible language.  Most hallucinations happen because the model is forced to guess an answer to a question without grounded information.

RAG-based retrieval changes this dynamic fundamentally by injecting authoritative evidence when the LLM answers the question. Instead of relying on memory or probability, retrieval pulls information from real documents, surfaces the exact passages and provides factual grounding for generated answers. As previously mentioned, this also makes “I don’t know” answers a possibility as the system can detect if no relevant knowledge or evidence exists. Finally with retrieval, answers must point back to real sources, enabling citations and traceability.

Scattered Knowledge That’s Invisible to AI

While RAG can mitigate some problems associated with general-purpose AI, it also creates opportunities to derive new insights from data across the organization. Enterprises have knowledge scattered in places like SharePoint/Google Drive, PDFs, ticketing systems, CRMs, wikis, emails and other internal tools. General-purpose AI can’t see across the silos of your knowledge, and typically context windows limit the amount of information you can pass to an LLM in each interaction.  Also, most content is written for humans with human understanding, assuming that you understand prior context, internal terminology  and the structure of information (tables, headers, footnotes, etc.) When translated to answers by  general-purpose AI, the meaning of this content can break apart.

Solving this challenge requires retrieval that goes beyond simple search and semantic matching.  An agentic retrieval layer connects to knowledge wherever it lives and treats distributed systems as a single, governed knowledge layer, rather than isolated silos. Human-authored content is ingested with an understanding of structure, semantics and domain language, which helps to preserve meaning across documents, tables, headers and footnotes instead of flattening it into disconnected text. At query time, retrieval strategies are used to assemble the most relevant context for each question.

Traditional Search Lacks Context and Answers

Traditional enterprise search was designed to return documents, not deliver answers. If you’ve used SharePoint in the past, you understand the struggle. Employees are presented with long lists of files, PDFs and links. Then you are expected to read, interpret and stitch together information on your own. Critical context is often spread across multiple documents forcing you to jump between sources, while hoping you don’t miss something important. Even when search results are technically “relevant,” they rarely surface the specific passage or business logic needed to confidently answer a real-world question.

Retrieval transforms search from a document discovery tool into a business answer tool. Instead of returning entire files, retrieval operates at the paragraph level, identifying and prioritizing the exact sections of content that matter the most. Multiple retrieval techniques can work together to combine semantic understanding, keyword precision, metadata or label filtering, and multi-step evidence gathering to pull relevant information from systems and sources. This retrieved context is then assembled into a coherent knowledge set before any reasoning or generation occurs. This enables AI to produce grounded, accurate answers backed by real evidence.

Why Retrieval Strategy Matters More Than Your LLM

Many enterprise AI initiatives focus on choosing the “right” language model, assuming better generation will lead to better answers. However, even the most advanced models are limited to the information provided to them at the time of the query. If the right knowledge isn’t retrieved, or if the knowledge is missing context, structure or relevance, the model is forced to guess—leading to hallucinations. This is why swapping models rarely fixes accuracy, trust or adoption issues in real enterprise environments.

What separates experimental AI from production-ready is not model intelligence, but retrieval intelligence. Advanced retrieval strategies actively interpret user intent, select the appropriate way to search and gather evidence across multiple sources before any answer is generated. Instead of relying on a single retrieval method, intelligent systems orchestrate multiple approaches such as semantic, keyword, filtered and multi-step. This allows AI to reconstruct the full business context behind a query,  rather than returning isolated fragments of information.

As enterprise use cases grow more complex, retrieval must also become more autonomous. Systems need to be able to reason about what information is missing, retrieve additional context when necessary and validate that answers are grounded in authoritative sources. This agent-driven approach to retrieval results in AI responses that are not only accurate, but explainable, auditable, and aligned with how the business actually operates. In enterprise AI, retrieval strategy isn’t an implementation detail;  it’s the foundation that determines whether answers are trustworthy or unusable.

How Progress Enables AI That Works to Fix Retrieval

When retrieval can’t accurately surface, assemble and contextualize enterprise knowledge, even the most advanced AI systems produce incomplete or unreliable answers. By rethinking retrieval as an intelligent, agent-driven foundation rather than a simple search step, the Progress Agentic RAG solution enables AI that works in real business environments by grounding AI in enterprise knowledge, aligned with business context with trust built  in.

Built for Enterprise Knowledge, Not Generic AI

The solution enables organizations to access knowledge in the way it is structured inside real enterprises. In many organizations, knowledge is rarely cleaned, centralized or written with AI in mind. It spans departments, evolves over time and embeds business logic, exceptions and institutional context that generic models don’t understand. Rather than forcing enterprises to rewrite or restructure their content, the solution works with existing knowledge, capturing its intent, nuance and applicability so AI can interpret it accurately. This enterprise-first design facilitates AI outputs to reflect how the business actually operates, not an oversimplified or theoretical version of it.

Connects Across Systems without Creating New Silos

Enterprise knowledge lives across a fragmented ecosystem of systems, including document repositories, collaboration tools, operational platforms and line-of-business applications. The Progress Agentic RAG  solution enables organizations to create a unified knowledge fabric, instead of isolated silos. By retrieving across your knowledge base, it eliminates blind spots that occur when AI is limited to a single repository or index. More importantly, it assembles information across sources into a coherent context, allowing answers to reflect the full picture, rather than a partial view.

Preserves Human-Written Structure and Context

Most enterprise content is authored for human readers, in turn  relying heavily on structure and shared understanding. Tables convey rules, headers define scope, footnotes introduce caveats and terminology carries implicit meaning. The solution preserves this structure during ingestion, preventing content from being flattened into disconnected text fragments. By maintaining semantic and structural relationships, the system facilitates  meaning to survive the transition from document to AI-ready context. This allows AI to interpret policies, procedures and guidance as they were intended, while respecting nuance, conditions and dependencies.

To achieve this, the solution applies purpose-built ingestion strategies (e.g., AI-driven table interpretation, article-level selection and vector-optimized language models) that adapt to the structure and intent of each source. Rather than treating all content the same, the system determines how knowledge should be parsed, indexed and retrieved so it remains usable by AI in real-world scenarios.

Reconstructs Complete Answers, Not Partial Results

Enterprise questions rarely have simple, single-source answers. They often require combining a base policy with exceptions, updates, amendments and historical context spread across knowledge assets. The Progress Agentic RAG solution retrieves and assembles this information before reasoning and generation begin, reconstructing the full business reality behind the question. Instead of presenting isolated facts or fragments, the solution provides answers that reflect all relevant conditions and dependencies. This dramatically reduces follow-up questions, escalations and manual verification, enabling users to act with confidence on the first given response.

Dramatically Reduces Hallucinations Through Grounded Retrieval

As previously mentioned, hallucinations occur when AI lacks sufficient or authoritative context and is forced to guess. The solution addresses this problem upstream by prioritizing grounded, evidence-based retrieval. By assembling the most relevant and authoritative sources before an answer is generated, the system minimizes ambiguity and removes the need for inference. This grounding not only improves accuracy but also builds trust with users who need to understand where answers come from and rely on their validity.

Now What? Go from Experimentation to Enterprise-Ready AI

Enterprise AI succeeds or fails at the point of retrieval. When knowledge is fragmented, context is lost and answers are assembled from incomplete information; even the most advanced models fall short. By fixing retrieval at the foundation with the solution, enterprises can move beyond experimental AI toward solutions that are accurate, explainable and trusted. This shift transforms AI from a novelty into a dependable business capability, unlocking real outcomes across teams’ workflows and use cases.

You can try the Progress Agentic RAG solution with a 14-day free trial. Try it for yourself today.