Polcom RAG, czyli praca modeli AI na danych firmowych
RAG, or Retrieval-Augmented Generation, is an approach that enables a language model to be linked to an organisation’s internal knowledge sources. This allows the model to generate responses based on its general knowledge as well as on documents, procedures, regulations, instructions, knowledge bases, reports and other company resources.
In practice, RAG facilitates the development of AI solutions that provide more relevant answers to questions about a specific organisation. Instead of training a model from the beginning, the system retrieves the relevant information from the provided sources and then uses it to generate a response. The primary benefit of RAG is that it enables organisations to capitalise on the capabilities of language models without the need to share data with public AI tools. The primary benefit of RAG is that it enables organisations to capitalise on the capabilities of language models without the need to transmit data to public AI tools. Organisations can develop solutions that draw on their own knowledge resources, whilst retaining greater control over the information that is fed into the model and used in its responses.
Polcom AI Cloud can support RAG implementations in scenarios such as:
- AI assistant for employees,
- searching corporate knowledge,
- analysis of internal documents,
- automation of customer enquiry handling,
- support for legal, technical, HR, sales and customer service departments,
- working with product, regulatory or design documentation,
- building secure AI tools based on the organisation’s data.
Dedicated instances and consistent model performance
In AI solutions deployed in production, the consistency of model responses is of great importance. This applies to the time taken to receive the first token and the number of tokens generated within a given timeframe. In shared environments, these parameters may vary depending on the load placed on the infrastructure by other users.
Polcom AI Cloud facilitates the allocation of dedicated computing resources, empowering organisations to meticulously plan application throughput and optimise the performance of their AI environment. Reserving capacity for a specific customer helps to reduce the volatility typically associated with public cloud solutions.
Key features:
- Stable performance metrics – consistent TTFS (Time To First Token) and TPS (Tokens Per Second) values ensure predictable application performance.
- Performance isolation – computing resources can be reserved exclusively for a single customer.
- Better load planning – dedicated infrastructure facilitates the design of AI applications intended to operate stably in real time.