# Data and knowledge management in the LLM era - VectorCat Blog

> LLMs and research data integration: what we learned working with life-sciences data.

Source: https://www.sci2sci.com/blog/data-and-knowledge-with-llm

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# Data and knowledge management in the LLM era

Angelina, CEO at sci2sci July 10, 2025 6 min read

AI Knowledge management Biopharma Scientific Data

LLMs and research data integration: what we learned working with life-sciences data.

# Data and knowledge management in the LLM era

Last time, we established that life sciences data is unbounded. We have our LIMS, but the critical _why_ behind an experiment often lives in a PowerPoint slide, a spreadsheet or a free-text note.

While there are many resources on managing data warehouses and LIMS/ELN systems, the topic of how to connect them to each other - and to other sources of context - is often missing. You won’t see sophisticated knowledge graphs or data fabrics in this letter. They are great - but they need a foundation, and while it’s easy to implement, it’s surprisingly rare to see it established.

I’ll explain what AI has solved - and what it hasn’t. LLMs have changed how we work with unstructured data. But if the data is missing or unfindable, they won’t offer much help.

## Contextual Identifiers and Connectivity

Identifiers are important - and tricky to get right. Most companies have identifiers by entity type: experiment ID, order ID, compound ID. That’s good, but it’s critical to establish IDs for processes too.

### Example:

Consider this workflow: prepare samples → send to a CRO → analyze results.

**Company A** has IDs for the preparation protocol, CRO order and analysis results.

**Company B** has all the same IDs, but also consistently mentions “Project-XXX” in every system.

**When the coordinator leaves:**

-   **Company A** spends weeks connecting the dots. Even with AI, it takes hours or days.
-   **Company B** connects everything in hours without AI. With AI integrated - minutes or seconds.

### Why the difference?

Company B preserved the information that existed only in the coordinator’s head. Company A has to restore it through painful archaeology.

Same data. Same process. The only difference: Company B had a rule of mentioning internal project IDs everywhere - from documents and spreadsheets to databases.

## Templates and Versioned Metadata Capture

Templates are needed to make decisions like “include project ID” automatic and not an additional cognitive burden.

### Implementation strategies:

-   **PowerPoint**: Add a template slide with relevant metadata as the last slide.
-   **Excel**: Include an additional sheet with required metadata.
-   **Documents**: Add metadata in headers.
-   **ELN**: Make it part of protocols or additional fields.
-   **Databases**: Establish metadata best practices in documentation.
-   **Naming conventions**: Create standard formats for files, samples and experiments (e.g. `ProjectXXX_YYYYMMDD_ExperimentType_Version`).

Templates are crucial for change management. They bridge the gap between “good idea, forgotten in a week” and “established practice” - but only if they’re extremely easy to apply.

### Key requirements for effective templates:

-   **Non-limiting**: Templates must be convenient to fill in, or they will be filled in poorly.
-   **Include free-text space**: With LLMs, we can finally process this easily, making it valuable for capturing deviations and non-standard situations.
-   **Versioned**: Easy to evolve over time - since it’s hard to predict what data you’ll need in the future.
-   **Fill once**: Avoid duplicate data entry. Use existing information wherever possible.
-   **Consistent naming**: Naming conventions should be simple enough to remember and apply without needing to look them up.
-   **Start simple**: Make everything traceable to the project, owner email and dates. A clear structure is a great starting point for automation, and LLMs make free text an accessible first-class data source. Then add the payload flexibly - branch the templates from a single core.

## Visibility and Easy Interface for Cloud Data

Researchers won’t learn new systems. If you have automated data pipelines doing BLAST over sequencing data - provide an easy search and chat interface on top.

This dramatically reduces “Where is the data?” questions. Engineering teams should do engineering - not act as chatbots for data requests. Turning queries into results from natural language is a solved problem with LLMs - let chatbots handle chatbot tasks.

### Implementation approaches:

-   Make it accessible via MCP to the company LLM
-   Integrate with company chat systems
-   Establish interfaces that can be extended over time

## The Takeaway

LLMs have provided great capabilities - structuring free-text documents, enabling conversational interfaces and simplifying interaction - but they’ve also highlighted the cracks in our general information management.

### Key principles that remain constant:

-   If data connectivity is lost, it will always be hard to recover
-   If critical metadata wasn’t captured, nothing will restore it
-   If interfaces aren’t easy - people won’t use them

### Which gives us three strategic directions:

1.  **Preserve cross-system data connectivity**
2.  **Develop processes to evolve metadata standards**
3.  **Implement lightweight interfaces that reduce manual work**.