Knowledge has become an organisation’s most valuable asset.

The ability to efficiently manage, retrieve and use this knowledge plays a critical role in ensuring innovation and productivity. The need for efficient knowledge management has led to the rise of advanced technologies such as Retrieval-Augmented Generation (RAG). RAG brings superior enhancements when it comes to LLM predictions, offering valuable benefits to R&D teams.

But what exactly is RAG and how can it revolutionise knowledge management in R&D teams?

Central to the world of NLP and AI, RAG blends the techniques of retrieval with generation to facilitate world-class message completion capabilities. It’s like having a master librarian who can not only find the best book in the library, but also distill its essence and present it to you, saving you hundreds of reading hours!

Let’s delve deeper into three compelling use cases where RAG’s retrieval capabilities and content generation functionalities can significantly enhance the efficiency of R&D workflows, particularly when used with proprietary data.

1. Centralised knowledge repository for projects

Think of RAG as your R&D team’s personal librarian. By creating a dynamic, real-time database, RAG allows you to centralise every iterative step and piece of knowledge attained throughout a project’s lifecycle. This function leads to a time-efficient solution where teams don’t need to sift through masses of unorganised data.

Example: A medical research team working on a new treatment.

Use Case: They could use myReach and RAG to organise the vast amounts of research data. This would ensure that even a year into the project, a single query could provide the entire experimental synopsis, highlighting crucial turning points and illuminating the path for future research. All, with just one search.

2. Automated research summarisation

The ability to swiftly search, retrieve and summarise internal and external research findings is crucial for R&D teams. This allows them to leverage previous work and theories. In this light, GenAI can prove invaluable, enabling the creation of a search user interface or a chatbot that uses a repository of past R&D results, previous projects and research papers to provide responses to complex queries.

Example 1: An R&D team working on a complex project that’s building on previous work.

Use Case 1: With RAG, the team can query past data and automatically generate a summary of those findings, drawing out essential insights pertinent to the current project. With myReach’s AI, it doesn’t matter whether the data is from last week’s project or from 2 years ago - if it’s saved in myReach the AI will retrieve it and find the relevant content to answer your question.

Example 2: A pharmaceutical research team developing a new drug.

Use Case 2: With thousands of research papers, prior cases and clinical tests to consider, RAG can retrieve relevant documents, generate summaries or key insights and allow the team to focus more on developing the drug, rather than on data mining and analysis.

3. Patent / process compliance reviews

RAG can also significantly enhance the efficiency of ensuring an organisation’s adherence to patent laws and process compliances, making it a helpful tool for internal audits.

Example: A legal department ensuring correct data protection compliance in the company.

Use Case: Using myReach, they can ensure the company's data handling processes comply with new data protection regulations like GDPR. All relevant documents, guidelines and communication are organised in one place. This ensures that, even months into the project, a single search can retrieve all critical updates, legal advice and procedural changes, helping them stay compliant and secure sensitive data effectively.

By automating the retrieval and analysis of legal documents, they can easily retrieve relevant patent laws, standards, or process guidelines and match them against current practises or products to ensure compliance, thereby removing the grunt work from the team.