The interpretive lens is a text field where you tell getbased what perspective you want the AI to consider. You can name researchers, clinicians, schools of thought, or health priorities. getbased includes that text in Current Focus, chat, and marker explanations.
Where to find it
Open Insight → Manage → Context, then click Interpretive Lens. You can also add a context widget to the dashboard and open the lens from there.
Click Interpretive Lens to open the editor, type or paste your lens text, then click Save. The saved lens is included in AI context immediately.
What to write
The lens is free-form — write the names of experts, schools of thought, or scientific paradigms you follow. Some examples:
- Researchers or clinicians focused on functional medicine, mitochondrial biology, hormesis, or longevity
- Scientific frameworks such as circadian biology, evolutionary medicine, or ancestral health
- Dietary or lifestyle philosophies you practice
- Any combination of the above
You usually do not need to explain well-known researchers or frameworks. If the model recognizes them, it can use that as context when answering.
How the lens changes AI analysis
Every AI interaction includes your lens text as part of the context when an AI provider is connected. The AI will:
- Frame its analysis through the paradigms you listed
- Draw on the research traditions those experts represent
- Flag where your results align or conflict with those frameworks
- Prioritize the markers and factors those frameworks consider important
For example, if your lens includes “circadian biology, UV and mitochondrial health, ancestral diet”, the AI will consider light exposure, vitamin D, and metabolic markers through those frameworks rather than giving you a standard clinical reading.
Relationship to the knowledge base
The interpretive lens tells the AI which perspective to use. A connected knowledge base takes this further by giving the AI actual passages to reason from — excerpts from research papers, clinical guides, or any documents you have collected.
When a knowledge base is active, every chat question and Current Focus refresh triggers a search of your documents. getbased finds relevant passages, sends them with your lab context, and asks the AI to cite them in its response.
This is called RAG — retrieval-augmented generation. In plain terms: getbased searches your documents first, then gives the best matches to the AI before it answers.
Think of it like a research assistant: you ask a question, they pull the most relevant pages from your library, and the AI reads those pages before answering.
Export and import
Your interpretive lens text is included in your JSON export and restored automatically on import. 