Large-scale language models (LLMs) such as GPT-4 or Gemini have revolutionised our interactions with technology, offering unprecedented capabilities in natural language processing. However, their knowledge has limits — it covers only data available at the time of training. This means they don’t understand the latest events, but above all they don’t know your company’s private data: documents, reports, invoices, procedures or contracts. In practice, it is precisely this information that is most valuable in decision-making processes.
The solution is Retrieval-Augmented Generation (RAG) — an approach that supplements and strengthens the capabilities of LLMs. RAG also reduces the problem of so-called hallucinations (seemingly credible but false answers). Thanks to integration with external and internal data sources, a RAG system allows answers to be generated that are grounded in current, contextual and secure company information.
This synergy breaks through the limitations of purely generative models and opens the way to more reliable AI interactions — both in customer service and in the work of analysts and managers.
Retrieval-Augmented Generation means LLMs become not only clever content generators, but also an active interface to the organisation’s knowledge base, operating on documents in any language.
TL;DR
- RAG strengthens LLMs through integration with external and company data, delivering more accurate answers and eliminating the hallucination problem.
- A typical RAG system consists of a retrieval module, an augmentation module and an answer generation module — effectively transforming documents into useful knowledge that supports decision-making processes.
- Contextual data often includes private company resources that language models don’t know on their own — RAG enables their secure use.
- The latest implementations use modern vector databases (e.g. pgvector in PostgreSQL, LanceDB) and built-in answer quality monitoring mechanisms.
- RAG has applications ranging from QA and contextual search to fact-checking, controlling and onboarding new employees.
- Building a PoC is easy, but production deployment requires enterprise-grade architecture and security.
- Recordya is a ready-made RAG system that works with files in multiple languages and is available immediately for your company.
Decoding Retrieval-Augmented Generation (RAG)
Modern language models learn from enormous datasets, accumulating broad knowledge in so-called parametric memory (neural network weights). However, on their own they have no access to current or internal organisational data. This leads to inaccuracies or incomplete answers, especially in areas requiring specialist knowledge.
The traditional approach — fine-tuning the model on private data — is costly, time-consuming and inflexible. That is why Retrieval-Augmented Generation is increasingly being used — proposed in 2020 by Facebook AI Research, UCL and NYU.
RAG combines a generative model with a retrieval module, allowing the system to dynamically reach for relevant company documents and current external data. As a result, answers become more precise and retraining ceases to be a necessity.
Main RAG components:
- Retrieval: retrieves relevant context from the document base.
- Augmentation: combines the user’s query and context into a prompt.
- Answer generation: the LLM creates a result grounded in the organisation’s data.
Thanks to this, RAG enables access not only to public knowledge, but above all to the company’s most important resources — those that constitute its competitive advantage.
The role of contextual data and document processing in RAG
In Retrieval-Augmented Generation systems, contextual data plays a key role — it is what expands the capabilities of language models and allows them to be used in real business processes.
Why are external and internal data so important?
Language models on their own don’t know a company’s private resources:
- financial and controlling documents,
- research and technical reports,
- contracts, regulations and legal clauses,
- internal procedures and employees’ expert knowledge.
It is precisely this information that is the foundation of decision-making processes in a company and builds the organisation’s intellectual capital. Without access to it, AI is unable to provide the right information to support real decisions by managers or operational teams.
RAG fills this gap because it combines:
- parametric knowledge (embedded in the model during training),
- non-parametric knowledge (stored in databases — e.g. vector repositories created from company documents).
This makes it possible to analyse and generate answers based on current, internal and secure data — without the need to share it with language model providers.
What benefits does this bring?
- Access to current information — RAG enables the latest files, reports and procedures to be included in the analysis process, without costly retraining.
- Security and control — data remains within the organisation (e.g. with local on-premise deployments), guaranteeing regulatory compliance.
- Building competitive advantage — the company uses its unique know-how, which becomes available to employees in the form of instant answers.
- Multilingual context — Recordya allows work with documents in various languages (e.g. Polish, English, Spanish), reflecting the real needs of global teams.
Why does this change the game?
In practice this means RAG becomes a next-generation knowledge base — not limited to what the AI knew at the time of training, but drawing on what actually exists in your company here and now.
This approach fundamentally changes the way knowledge and decision-making processes are managed:
- information is always up to date,
- employees have access to context without tedious searching,
- knowledge doesn’t disappear when staff turn over, but stays within the organisation.
How a RAG system works
RAG systems combine the power of language generation by LLMs with the precision of contextual document search. This allows them to answer user questions not only based on general knowledge, but also on the company’s private resources.
Main phases of a RAG system
Loading documents and dividing content into fragments
Documents from various sources (e.g. PDFs, reports, invoices, notes, databases) are loaded into the system. Text is divided into smaller fragments (so-called chunks), enabling fast searching and analysis of relevant information.
Transforming text into vectors (embeddings)
Document fragments are converted into numerical representations (vectors) by embedding models. This allows the computer to analyse semantic relationships between the user’s query and the content in the knowledge base.
Searching the vector database
Recordya uses modern database technologies such as pgvector (PostgreSQL) or LanceDB, which allow fast searching for the most relevant fragments. Searching takes place not only by keywords, but also by the meaning of content, which eliminates the problem of “surface” matching and increases answer accuracy.
Query augmentation
The user’s query is enriched with context retrieved from the document base. A prompt containing company data is created and sent to the language model.
Answer generation
The LLM, using both its general knowledge and the provided document fragments, creates an answer. The user receives an instant, reliable and contextual piece of information based on the company’s real data.
Different response strategies
Depending on the complexity of the question and the volume of documents, a RAG system can use different methods:
- Stuffing — simple injection of context into the prompt (best for short queries).
- Map-Reduce — dividing large documents into fragments, generating partial answers and combining them into a whole.
- Refine — iterative supplementing and improving of answers.
- Map-Rerank — assessing the relevance of multiple fragments and selecting the best ones.
Thanks to these techniques, RAG is able to handle both quick operational questions (“Which contract expires this quarter?”) and complex strategic analyses.
Why does this work?
This approach allows employees to quickly reach the right data, without having to search through dozens of documents themselves. As a result, the system becomes a knowledge base that supports decision-making processes, streamlines knowledge transfer to new employees and automates document processing in the organisation.
The impact of RAG on natural language processing and knowledge management
RAG fundamentally changes the way organisations use information. It is no longer just about generating content, but above all about building a knowledge base that supports decision-making processes in the company and enables the real use of the organisation’s intellectual capital.
RAG in Polish — why is this a unique challenge?
Analysing the Polish language poses additional barriers for AI systems. Polish is inflectional, rich in declensions and allows variable word order. This means:
- simple chunking methods can lose important information,
- traditional approaches based only on keywords cannot handle the full meaning of content,
- large language models, trained mainly in English, have limited knowledge of Polish semantics and formal styles (e.g. legal or technical language).
That is why RAG in Polish is not just a translation of technology, but its genuine adaptation to the specifics of the language.
Recordya solves this problem because it:
- uses contextual search mechanisms instead of just keyword matching,
- handles legal, financial and technical documents better,
- works multilingually (PL/EN/ES), but is above all a ready-made RAG system in Polish for your company.
The advantage of RAG in decision-making processes
Thanks to this approach, employees find it easier to find the right information, even if they formulate user queries in different ways. In practice this means:
- instant answers to questions based on current documents,
- real support for the decision-making processes of managers and analysts,
- the ability to analyse and generate content using the company’s private resources,
- competitive advantage over organisations that rely only on the general knowledge of language models.
Main RAG applications
- QA (Question Answering) — answers to user questions directly from the organisation’s documents.
- Contextual search — instead of keyword matching, the system uses semantics and text relationships.
- Summarising and extracting content — managers receive report summaries with the most important data highlighted.
- Onboarding new employees — faster knowledge transfer and socialisation into the company’s organisational culture.
- Controlling and finance — automating document processing: invoices, reports, statements.
- Customer service — knowledge base available to consultants and customers anywhere, anytime.
Why is this important for your company?
RAG is not just technology, but an approach to knowledge management. Thanks to it, your company can:
- use its own document resources powered by AI,
- reduce the need for manual document searching,
- support employees in decision-making,
- develop a knowledge management strategy that provides lasting competitive advantage.
RAG applications
Although theory is important, the real value of RAG only becomes apparent in practice. By implementing RAG, companies can change the way they manage knowledge, process documents and support decision-making processes.
1. Handling user queries
Every day, questions arise in companies: “Which contract expires this quarter?”, “What were the costs in the previous report?”, “Where do I find the security procedure?”
Thanks to RAG, answers are generated based on document databases and delivered as instant answers. This reduces the need for manual searching and gives employees quick access to relevant information.
2. Contextual search in large repositories
Instead of classic keyword matching, the system analyses semantics and text relationships. This means that even if questions are formulated differently from the documents, RAG will return the relevant information.
As a result, organisations gain competitive advantage because employees can act faster and with greater confidence, using the full context.
3. Summarising and extracting data from documents
Financial reports, technical analyses or research studies can run to hundreds of pages. RAG allows content to be analysed and short summaries generated that help managers and analysts make decisions without having to read the whole document. This is an example of how artificial intelligence is used to save time and increase efficiency.
4. Onboarding new employees
New people in the organisation often spend weeks understanding documentation and processes. A knowledge base built on RAG enables quick retrieval of procedures, regulations and instructions. As a result, employees find it easier to go through the socialisation process, and companies shorten the implementation time for new staff.
5. Controlling and finance
In the controlling department, RAG automates the searching and processing of documents: invoices, reports, contracts. This allows analyses to be prepared faster, anomalies to be identified and financial decision-making processes to be supported. This demonstrates how RAG uses natural language processing in a practical way, directly linked to business results.
6. Customer service
RAG becomes a knowledge base that works not only for employees, but also for customers. Consultants and self-service systems deliver instant answers based on current procedures and documents. This improves service quality and increases customer satisfaction, because knowledge becomes available anywhere, anytime.
7. Working with sensitive and confidential data
In many industries, the data worked with on a daily basis cannot be passed to public language models.
Law firms need instant access to contracts, clauses and case law, but their content must remain confidential.
R&D laboratories and research and development teams work with reports, experiment results and technical documentation that no LLM trained on open datasets knows.
RAG solves this problem because it feeds the model with context from the organisation, while data remains within the company’s secure knowledge bases. This allows companies to use the full power of artificial intelligence without exposing their trade secrets and know-how.
RAG deployment architecture
Building a simple PoC based on open-source is one thing, but deploying a production RAG system in an enterprise is a completely different scale of challenges: security, scalability, integrations with existing systems.
That is why Recordya was designed as a ready-made tool that eliminates the need to build the entire infrastructure from scratch.
Main elements of Recordya’s architecture
Local or cloud deployment
Recordya can operate fully on-premise, delivered along with a device (so-called appliance) that can be installed in the company’s infrastructure. Alternatively, the system can operate in a secure cloud environment. In both cases, the organisation retains full control over information resources and private documents.
Support for multiple document formats
The system automatically reads, analyses and indexes documents in various formats: PDF, Word, Excel, emails, text files and even databases. This means company knowledge is consolidated into one coherent knowledge base.
Vectorisation and contextual search
Documents are divided into fragments and transformed into vectors that go into a vector database. Recordya uses modern solutions (e.g. pgvector, LanceDB) to enable contextual search instead of simple keyword matching.
Integrations with company systems
Recordya has basic integrations with popular document and process management tools (e.g. SharePoint, Jira, Slack, ERP/CRM systems). This allows the model to be fed with current data without the need to manually import files.
Security and quality monitoring
Data remains within the organisation — regardless of whether the deployment is local or cloud-based. Built-in answer quality monitoring ensures employees receive the right information, and the system continuously learns from interactions.
What does this mean in practice?
- Data security — confidential documents (contracts, R&D reports, invoices) never leave the company environment.
- Instant answers for employees — the system immediately searches all sources and delivers precise results.
- Time savings — automating document processing eliminates the need for manual searching and content analysis.
- Scalability — the architecture allows the system to be developed in line with the organisation’s needs.
Challenges in RAG deployments
Although the idea of Retrieval-Augmented Generation seems simple, implementing it in an enterprise encounters several serious technological challenges.
1. Data security
Companies work with confidential documents — from legal contracts, through R&D reports, to invoices and financial analyses. In classic approaches, the risk of data leakage to the public cloud is real. Recordya solves this problem by offering the possibility of full on-premise deployment or in a closed cloud environment, where information resources never leave the organisation’s infrastructure.
2. Scalability and performance
Simple PoCs usually operate on a limited sample of documents. In production, however, it is necessary to handle:
- thousands of files in various formats,
- hundreds of user queries simultaneously,
- the need for constant knowledge base updates.
Recordya is designed as an enterprise-grade system that scales with the organisation’s growing needs.
3. Computational costs
RAG combines machine learning with intensive vector searching. An improperly designed architecture quickly generates high computation and data storage costs. Recordya optimises processes: it uses intelligent text splitting, result caching and modern vector databases, which reduces infrastructure load and lowers costs.
4. Variety of data formats
Companies use a wide range of sources — PDFs, Excel spreadsheets, emails, databases, ERP and CRM systems. Building your own parser for all these formats is time-consuming and requires high technical competence. Recordya immediately reads, analyses and indexes documents in many formats, combining them into one coherent knowledge base.
The future of RAG in practice
Retrieval-Augmented Generation is not a passing trend, but a lasting direction of AI development in knowledge management. Companies already need tools that:
- ensure data security,
- support decision-making processes,
- automate document processing,
- and operate based on their own information resources.
What will be key in the coming years?
Integration with business systems
RAG will no longer be a separate layer, but will become part of companies’ everyday ecosystem. Recordya already offers integrations with popular document management, ERP and CRM tools.
Answer quality monitoring
Ensuring that AI answers are accurate, consistent and auditable is becoming increasingly important. Recordya has a built-in mechanism for monitoring the quality and history of user queries, enabling control and continuous system improvement.
Agent-based systems built on RAG
The next step will be the automation of business processes — e.g. a financial agent that independently prepares a report based on invoices, or an HR agent supporting the onboarding of new employees. Recordya is the foundation that enables the development of such applications.
Secure local deployments (on-premise)
In an era of growing regulations and compliance requirements, the ability to use local databases and private document repositories will become increasingly important. Recordya was designed from the outset with this scenario in mind.
Multilingualism and local specifics
RAG in inflectional languages (like Polish) is a challenge that will be raised more and more frequently. Recordya already delivers RAG in Polish, also working with documents in other languages, which responds to the real needs of global teams.
How to implement a production RAG system in your company
Many organisations experiment with their own RAG-based PoCs, but only a few are able to move from prototype to a stable, secure solution operating at scale. This is precisely the moment where Recordya makes the difference.
Why Recordya?
- Ready-made tool — you don’t need to build infrastructure from scratch.
- Secure on-premise or cloud deployment — full control over private data and documents.
- Support for multiple data formats — PDF, Word, Excel, emails, databases and ERP/CRM systems.
- Integrations with your systems — Recordya works within the ecosystem you already have.
- RAG in Polish — tailored to the specifics of the language and documents in your company, while also supporting multiple languages.
- Enterprise-grade scalability — regardless of whether you have hundreds or millions of documents.
What does your company gain?
- A knowledge base that answers user queries and supports all decision-making processes.
- Automation of document processing, allowing employees to focus on solving problems rather than searching for files.
- Competitive advantage, because you use your own intellectual capital faster and more effectively than others.
- Instant answers available to employees and customers anywhere.
Start today
If you want artificial intelligence to genuinely support knowledge management in your company, choose a solution that is: proven, secure, and ready to use immediately.
👉 Contact us and book a Recordya demo — see how easily your company can gain its own next-generation knowledge base.
