Background information
on Retrieval-Augmented Generation (RAG)
Retrieval-Augmented Generation, or RAG for short, is now one of the most important architectural patterns for the productive use of generative AI in business. The reason is simple: a language model excels at understanding language, summarising content, structuring it, transforming it and generating natural-sounding responses. However, what a language model alone cannot reliably do is access up-to-date, company-specific, comprehensive and verifiable knowledge at any time. This is precisely where RAG comes in.
How it works
In a traditional large language model, the ‘knowledge’ essentially stems from the model’s training. During its training, the model has therefore learnt statistical patterns, correlations and a wealth of facts. However, this knowledge base is limited. It may be out of date, it does not automatically provide an up-to-date view of your internal documents, and it is generally not precise enough to reliably answer detailed questions that are critical to the business. In practice, this leads to one of the biggest hurdles in the use of AI within organisations: users expect reliable answers, whilst the model, lacking external context, formulates responses based solely on probabilities and thus ‘hallucinates’.
RAG closes precisely this gap. The basic idea is that, prior to the actual generation of an answer, not only is the language model used, but also a retrieval mechanism. This retrieval step searches relevant knowledge sources, identifies suitable content and makes it available to the model as context. The language model therefore responds not only based on its trained world knowledge, but on the basis of relevant information that has actually been found. This makes the answer more substantively sound, more transparent and significantly better tailored to the specific use case.
Put simply, a RAG process involves several steps. First, a user asks a question or triggers a process. This query is then converted into a format suitable for searching. A retriever then searches defined data sources, such as guidelines, PDFs, manuals, tickets, SharePoint content, wikis, CRM data or structured systems. The most relevant results are collected, prioritised and passed to the model as context. Only then does the LLM generate the response. Good RAG systems further enhance this process with filters, metadata, permissions, ranking, tool usage, memory, quality checks and domain logic.
Relevance for businesses
Why is this so important for businesses? Because productive AI almost always requires more than just text generation. In reality, it involves extracting specific information from contracts, work instructions, product data, knowledge bases, technical documentation, emails, processes, files or industry-specific regulations. It is about up-to-date and reliable information, comprehensible answers and the controlled integration of the right data at the right time. This is precisely why RAG is not an optional extension in many professional AI scenarios, but the very foundation.
There is also a second aspect: scalability. A company does not want to ‘re-train’ knowledge into a model from scratch for every use case. That would be expensive, slow and difficult to maintain. With RAG, existing knowledge sources can be made directly usable. New documents, updated guidelines or changed data sets can be integrated into the retrieval process without having to retrain a model each time. This results in faster update cycles, lower barriers to entry and significantly greater control over quality and governance.
Not all RAGs are the same
In practice, a whole family of different RAG architectures has emerged. These variants differ in terms of which data is connected, how searches are conducted, the degree of autonomy the system has in gathering information, whether session progress and user history are taken into account, whether multiple sources or media types are combined, and the extent to which a response is subsequently validated or enriched. Modern AI platforms must therefore not only be capable of ‘RAG’, but also flexibly support different types of RAG.
This is precisely where AIMAX® demonstrates its strength. As a central AI infrastructure solution, AIMAX® is designed for productive enterprise use. The platform has a modular structure, can utilise different AI models in parallel, integrates a wide variety of data sources and applications, and also supports numerous RAG types.
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The RAG types supported by AIMAX® in detail
As a general rule, RAG should not be viewed as an isolated, one-off solution, but rather as a strategic component of an AI infrastructure. Depending on the use case, a different RAG model may be appropriate within the same platform. Standard RAG is often sufficient for simple knowledge queries. For multi-stage processes involving the use of tools and decision-making logic, Agentic RAG is significantly more powerful. Memory Augmented RAG becomes relevant for personal assistants or recurring user interactions. Multimodal RAG is required for image documents, scans and complex file formats. And for highly interconnected knowledge domains or organisation-wide search spaces, further specialised forms come into play.
Standard RAG
Standard RAG is the classic approach and, in many projects, the first step into retrieval-augmented generation. The architecture follows a clear basic logic: a user query is searched for in the knowledge base, relevant passages are found and provided to the language model as additional context. Based on this, the model generates a response that is more closely tied to real-world information than is the case with purely generative processing.
The major advantage of Standard RAG lies in its ease of understanding and rapid implementation. For many knowledge assistants, internal FAQ systems, support copilots or document queries, this pattern is already sufficient. It delivers an immediately noticeable benefit, as answers are less prone to ‘hallucinations’, address corporate knowledge more accurately, and remain closer to documents or guidelines. Standard RAG is therefore often the productive ‘baseline approach’ upon which further optimisations are built.
The limitations of Standard RAG become apparent as soon as complex dependencies come into play. When multiple systems need to be queried, when an answer must be composed across several intermediate steps, or when different media formats are to be incorporated, the basic pattern quickly reaches its natural upper limit. Nevertheless, Standard RAG remains the most important reference architecture because almost all extended variants are based on the same core principle: retrieving relevant information, placing it in context, and generating a response.
Agentic RAG
Agentic RAG extends the classic RAG principle to include planning, goal-orientation and active use of tools. Unlike standard RAG, it does not simply search for knowledge once and then provide a response. Instead, an agent decides step by step what information is required, which retrievals should be carried out, which tools or systems need to be integrated, and whether further intermediate steps are necessary.
This architecture is particularly powerful when queries are complex, open-ended or ambiguous. An agentic system can break down a task, access multiple sources in sequence, evaluate interim results and derive the next steps from them. This transforms a mere knowledge assistant into an AI agent capable of taking action, which not only provides information but also intelligently supports processes.
For example, a query such as “Check which contracts expire in the next 90 days, summarise the key terms and conditions, and draft a renewal request” can be handled significantly better with Agentic RAG than with standard RAG. A single document match is not sufficient here. The system must find contracts, identify deadlines, extract relevant clauses, set priorities and then prepare appropriate communication.
The added value lies in the orchestration. Agentic RAG combines retrieval with decision-making pathways.
Memory-Augmented RAG
Memory Augmented RAG enhances retrieval with memory. This does not refer to static model knowledge, but rather to context-based recall of previous interactions, user preferences, roles, ongoing tasks or session histories. The system therefore accesses not only documents and data sources, but also a stored usage context.
This architecture is particularly valuable where interactions do not take place in isolation. An assistant that supports a user, a project, a case file or an ongoing process across multiple messages benefits enormously from not having to request relevant background information anew each time. This makes responses more personalised, consistent and efficient.
It is important to distinguish between short-term and long-term memory. Short-term memory typically covers the current session, i.e. the immediate course of the conversation. Long-term memory may also contain role profiles, task contexts, personalised settings, previous results or explicitly stored user preferences. In a professional platform, this memory must be managed in a controlled, transparent and data-protection-compliant manner.
For businesses, Memory Augmented RAG is particularly useful for personal assistants, case management, recurring support processes, project work, onboarding processes, or any scenario where the same individuals collaborate with the same AI over an extended period. As a result, the responses feel less generic and more firmly embedded in the actual work context.
Multimodal RAG
Multimodal RAG extends traditional retrieval beyond purely textual sources. In addition to continuous text, images, scanned documents, tables, diagrams, presentations, technical drawings, forms and screenshots can also be incorporated into the retrieval and response process. This is hugely important in practice, as organisational knowledge rarely consists solely of clean, structured text.
Much critical information is found in PDFs containing tables, in manuals with illustrations, in quality reports with diagrams, in forms with tick boxes, or in emails with attachments and screenshots. A text-centric RAG system can only capture such content to a limited extent. Multimodal RAG, on the other hand, processes different media types, extracts usable context from them, and makes this available to the model in a suitable form.
The strength of Multimodal RAG therefore lies in its practical relevance. It reduces the artificial distinction between ‘text-based’ and ‘non-text-based’ knowledge sources. Instead of manually converting information in advance, the AI can operate more closely with real documents and working materials. This is particularly relevant for companies with heterogeneous data sets, historically accumulated archives, or knowledge-intensive specialist departments.
Technically, Multimodal RAG requires additional processing steps, such as OCR, layout recognition, table extraction, image description or multimodal embeddings. The benefits, however, are significant: a much more comprehensive knowledge base is created. Particularly in a modular infrastructure with various integrations and channel options, this approach is a key component in ensuring that AI is not limited to ‘pretty text’, but is truly made enterprise-ready.
Federated RAG
Federated RAG is the right solution for distributed knowledge. In many organisations, relevant information is not stored in a central repository, but is spread across various systems, departments, locations, clients or specialist applications. A federated RAG approach therefore does not search a single index, but orchestrates access to multiple data spaces in parallel or in a coordinated manner.
In reality, this is often the crucial difference between a demo and a viable enterprise solution. A single query may require knowledge from Confluence, a DMS, a CRM, a ticketing solution and a specialist application. Federated RAG ensures that these sources interact logically without necessarily having to physically consolidate all data in one place beforehand.
The benefits lie in flexibility, scalability and governance. Data can remain where it belongs from a business or regulatory perspective, whilst the AI still makes the relevant information available across the board. This reduces integration barriers and supports scenarios where different responsibilities or security zones exist.
Contextual Retrieval RAG
Contextual Retrieval RAG focuses on a question that is often underestimated: under what conditions are searches actually carried out? In many RAG projects, the quality of the answers does not depend primarily on the model, but rather on whether the search query is formulated appropriately, enriched correctly and interpreted meaningfully. This is precisely where contextual retrieval comes in.
The basic idea is that a query is not considered in isolation. Instead, additional context and signals are incorporated into the search phase. These may include, for example, user role, department, channel used, product context, document type, language and time frame. This additional context influences which content is deemed relevant and how heavily it is weighted.
Contextual Retrieval RAG is therefore less of an exotic special case and more of a quality lever. It ensures that the system understands ‘what is actually meant here’ better even before generating a response.
Domain-Specific RAG
Domain-Specific RAG specialises in specific subject areas. Unlike generic RAG setups, this variant takes into account not just any documents, but domain-specific vocabulary, typical document types, technical rules, regulatory frameworks and industry-specific meanings. The system is therefore specifically optimised for a particular field of knowledge.
This is particularly important because the same terms can mean something completely different depending on the sector or department. In industry, law, tax consultancy, healthcare or public administration, linguistic precision and subject-matter classification are crucial. A generic search and response system often delivers superficial or misleading results in these contexts.
The concrete benefits are significant: answers become more reliable from a technical perspective, more precise in terms of terminology, and closer to real-world work requirements. At the same time, the risk of overlooking relevant nuances is reduced. For companies, this means that AI is not merely ‘somewhat helpful’, but operates within the language and logic of the respective specialist field.
Hybrid RAG
Hybrid RAG combines different search methods within the same retrieval logic. This usually involves combining semantic vector search with traditional keyword or full-text search. The rationale is clear: both methods have strengths and weaknesses. Vector search finds similar meanings, even if the exact terms are not identical. Keyword search is effective when precise wording, product numbers, paragraphs, technical codes or strictly defined terms are crucial.
In practice, this combination is extremely valuable. Many business queries contain both semantic and exact elements. A user might search for ‘the current travel expense policy for overnight stays abroad’, where the phrasing is flexible but the actual target passage contains an exact policy term. Or a service representative might search for an error description containing a specific code, whilst the rest of the context is described only vaguely.
For businesses, hybrid RAG is often the most pragmatic production solution because real-world knowledge repositories are never homogeneous. Some content is highly structured, some is text-heavy, and others are full of IDs, abbreviations or standard references. A hybrid approach takes this heterogeneity into account rather than ignoring it.
Self RAG
Self RAG extends the retrieval process to include self-assessment. The system not only answers a question, but also checks the quality of its own response. It can recognise when the evidence is insufficient, when there is uncertainty, or when further documents are required. In such cases, it triggers additional retrieval steps before formulating a final answer.
This architecture addresses one of the core problems of generative systems: they often respond fluently even when the basis is weak. Self RAG attempts to break precisely this automatic response. The AI should not only produce output but also reflect on whether the answer is actually sufficiently substantiated. Ideally, this leads to more cautious, better-supported and more reliable results.
Self RAG is particularly interesting for quality-critical environments. Wherever incorrect answers can be costly, risky or pose regulatory problems, an architecture with built-in self-checking is worthwhile. However, it is more complex because evaluation logic, thresholds and re-retrieval rules must be defined.
Graph RAG
Graph RAG combines RAG with knowledge graphs. This means that information is not merely stored as isolated document passages, but is also modelled in the form of entities and relationships. People, organisations, products, processes, places, documents or events can thus be explicitly linked to one another. This is particularly useful wherever contextual relationships are more important than individual text passages.
In traditional document searches, knowledge is often fragmented. Relevant information is scattered across different texts, and the actual insight only emerges when several elements are linked. Graph RAG makes precisely these links explicitly usable. The system can not only search for passages, but also take into account relational paths, dependencies, hierarchies or networks.
A typical use case is compliance or contract management. A query may simultaneously refer to involved companies, responsible contacts, contract versions, deadlines, annexes and referenced clauses. In pure document retrieval, such relationships are difficult to represent explicitly. A graph-based approach, on the other hand, can make these relationships systematically available.
“RAG is not just a technical method for document retrieval, but a strategic lever for reliable, scalable, and enterprise-ready AI. The targeted use of RAG types by the AIMAX® AI platform creates the foundation for artificial intelligence to not only impress, but also provide measurable benefits in everyday life.”
Conclusion: The right type of RAG for the right use case
RAG is not a single feature, but an architectural framework. Anyone wishing to deploy productive AI seriously within an organisation should therefore not merely ask whether a platform supports retrieval, but which form of retrieval is available for which use case. This is precisely where a key difference lies between simple AI demos and robust AI infrastructure.
For a modern AI platform such as AIMAX®, it is therefore not enough to implement just one of these types in isolation. What matters is the ability to combine different RAG patterns in a modular way, depending on the target scenario, and to embed them into existing systems, processes, roles and security requirements.
