RAG, CAG, and KAG—Oh My! A Medical Writer’s Journey Down the Yellow Brick Code
- Jeanette Towles
- 3 days ago
- 3 min read
Somewhere over the rainbow of prompt engineering lies a land where AI doesn’t just guess—it retrieves, contextualizes, and reasons. For medical writers venturing into this new world, understanding RAG, CAG, and KAG can make the path to clear, compliant writing a little less mysterious.
From Prompting to Purpose
Medical writers are no strangers to structure. We know that words alone don’t make a submission—data integrity, traceability, and consistency do. The same principle applies to artificial intelligence.
Most writers have tried generative tools that spin text from prompts, but behind that polished paragraph is a deeper layer of design. Modern AI systems use architectures that retrieve, contextualize, and connect information to improve accuracy, explainability, and compliance.
Three of the most relevant for regulated content are Retrieval-Augmented Generation (RAG), Context-Augmented Generation (CAG), and Knowledge-Augmented Generation (KAG). Each represents a step closer to how humans think when writing in a regulated environment: check your facts, understand your context, and maintain consistency across the narrative.
RAG: Retrieval-Augmented Generation: For When Accuracy Matters Most
RAG combines a large language model (LLM) with a retrieval system that pulls relevant information from verified sources before writing.
In regulatory and clinical writing, this can be transformative:
Clinical Study Reports (CSRs): RAG can accurately reference efficacy tables, patient disposition summaries, or adverse event listings without hallucination.
Integrated Summaries (ISS/ISE): Retrieval ensures internal consistency between data tables and narrative text.
Response-to-Health-Authority Letters: RAG can surface precedent responses or linked datasets to support evidence-based justifications.
In short, RAG ensures your AI “knows where it read that.” For medical writers, that’s the difference between automation and accountability.
CAG: Context-Augmented Generation: For When Structure and Tone Matter
CAG adds structured context—metadata, templates, and stylistic rules—to guide how AI composes. It doesn’t just write; it writes within boundaries.
In practice:
Enforcing plain language readability in patient-facing summaries.
Applying eCTD templates or label-aligned phrasing across multiple modules.
Maintaining consistent voice across collaborative authoring environments.
CAG is what makes AI a reliable co-author instead of an unpredictable intern. It helps ensure that content generated from one prompt aligns with established tone, structure, and quality expectations.
KAG: Knowledge-Augmented Generation: For When Connections Count
KAG represents a more advanced evolution, enabling AI to access a structured knowledge graph or ontology. Instead of relying only on retrieval or template constraints, it reasons based on the relationships between concepts.
In medical writing, this can look like:
Linking safety narratives to risk-benefit sections within a Clinical Overview.
Connecting nonclinical findings to clinical implications in integrated summaries.
Maintaining terminology consistency across multiple submissions or indications.
KAG helps AI think more like a regulatory strategist than a typist—it understands how sections inform one another, not just what they contain.
RAG, CAG, or KAG? Choosing the Right Approach
Approach | Best For | Example Applications |
|---|---|---|
RAG | Precision and data integrity | CSRs, appendices, responses to regulators |
CAG | Consistency and tone | Plain language summaries, eCTD templates, Module 2 summaries |
KAG | Connected reasoning | Nonclinical and clinical overviews, cross-functional summaries, integrated summaries |
The simple takeaway:
RAG retrieves. CAG contextualizes. KAG connects.
Together, they form the next generation of explainable, compliant AI for medical communications.
Why Medical Writers Should Care
As AI becomes embedded in authoring platforms, understanding these mechanisms helps writers maintain control over quality. Knowing how your AI is generating text—where it gets its information, how it structures its context, and what knowledge it draws upon—builds trust and reduces risk.
It also helps writers ask the right questions when evaluating tools:
Can I trace where this data came from?
Is the model using approved templates and terms?
Does it maintain consistency across connected documents?
In regulated writing, this isn’t a mere curiosity; it’s compliance.
The Road Ahead: From Prompting to Partnering
This post launches our 2026 AI Series for Medical Writers, where we’ll explore the machine learning concepts that matter most for those shaping the future of clinical and regulatory communication.
Next up: “Contrastive Learning for Clinical Writers—How AI Learns to Tell the Difference Between ‘Significant’ and ‘Meaningful.’”
Until then, you can explore related reads to keep building your AI literacy:
Partner with Synterex
At Synterex, we combine deep domain expertise with AI-enabled innovation through our flagship platform, AgileWriter.ai®—built by SMEs, for SMEs. Our solutions span structured content authoring, explainable automation, and AI-driven efficiency tailored to regulatory documentation and medical communications.
Let’s explore how your teams can move beyond prompting to planning, with traceable, compliant, and human-centered AI.