Key Takeaways
- Context is King for Accuracy: In insurance, fluency in natural language isn’t enough; AI must be grounded in real-world, up-to-date documentation to prevent hallucinations and ensure compliance.
- RAG Outperforms CAG at Scale: While Cache-Augmented Generation (CAG) works for small, static datasets, Retrieval-Augmented Generation (RAG) is superior for insurance because it handles growing document sets with 80-90% better cost efficiency and significantly lower latency.
- Precision Engineering is Required: Building a “production-grade” advisor requires more than just an LLM; it requires fine-tuning hyperparameters like chunking strategies and similarity thresholds to navigate dense legal boilerplate and nested tables.
- Bridging Semantic Gaps: Effective insurance AI must understand intent rather than just keywords—for example, recognizing that a query about “key loss” relates to “vehicle accessories” clauses.
As artificial intelligence becomes increasingly embedded in insurance enterprise workflows, one reality has become unmistakably clear from our work with insurers at CoverGo: context matters.
It is no longer sufficient for an AI system to be fluent in natural language. To deliver real business value, AI must generate answers that are grounded in the real-world and up-to-date information. Especially in the insurance industry, where accuracy directly impacts compliance, claims outcomes, and customer trust. Unsupported or outdated responses are unacceptable.
The Insurance Knowledge Challenge
At CoverGo, we see this challenge every day. Insurers manage vast volumes of complex and continuously evolving documentation, including policy wordings, claims procedures, underwriting guidelines, FAQs, regulatory disclosures, etc.
The challenge revolves around how to give the model a vast amount of information and context without the drawbacks. Supplying entire document sets comes with significant token costs, slower response times, and, counterintuitively, worse performance because of noisy or irrelevant information overwhelming the model.
This has led to a fundamental architectural question: How should large language models (LLMs) access insurance knowledge at scale?
Two primary approaches have emerged: Cache-Augmented Generation (CAG) and Retrieval-Augmented Generation (RAG).
Understanding the Architectures: CAG vs. RAG
Both RAG and CAG enhance LLMs with external knowledge, but they differ significantly in how that knowledge is accessed.
Retrieval-Augmented Generation (RAG)
RAG performs real-time retrieval (i.e., searches the contextual database) for every user query. Relevant document chunks are retrieved from the database and injected into the model’s prompt, ensuring responses are grounded in current and relevant source material.
Key characteristics of RAG:
- Real-time retrieval at query time, fetching information as needed.
- Naturally handles changing or growing document sets.
Cache-Augmented Generation (CAG)
CAG preloads knowledge—such as entire documents—into the model’s key-value (KV) cache ahead of time. The model then combines cached context with its pretrained knowledge to produce relevant answers without performing a retrieval step for each query.
Key characteristics of CAG:
- Pre-cached data that has been stored and used repeatedly.
- Fast for repetitive queries.
- Poor adaptability to frequently changing data.
Our Research: The Data-Driven Decision
Our internal research highlights why RAG architecture is better in the context of an insurance product expert chatbot.
| Feature | CAG | RAG | Why it matters for Insurance |
| Accuracy | 80–90% | 90–95% | Targeted retrieval reduces the “noise” that leads to hallucinations. |
| Latency | 10–18s | 4–6s | Internal staff and customers expect near-instant responses. |
| Cost | High Per-Query | 80–90% Savings | Efficient token usage enables sustainable scale. |
| Scalability | Limited (~60 docs) | Effectively Unlimited | Policies, riders, and addendums grow continuously. |
Cache-Augmented Generation is a good option for fixed, smaller knowledge bases, which are to be called upon frequently. In this setup, cached content gets re-hit repeatedly — often without the cache expiring — which makes it efficient for stable, predictable datasets.
However, CAG does not scale well once the knowledge base grows. At 50+ Docs (250k+ tokens), the performance degrades rapidly in terms of latency, costs, and mistake rates.
In document-heavy industries like insurance, where information is constantly evolving, this becomes a structural limitation. Cache-Augmented Generation models are constrained by context size and become expensive when caching large volumes of content.
RAG, on the other hand, is more scalable and configurable. With RAG we can control embedding strategy and chunking strategy. For RAG, the cost and latency are directly tied to what is actually retrieved — not the total size of the knowledge base.
For example, even with a 500k+ token corpus, we can retrieve only the top 20 relevant chunks capped at 1,000 tokens each. This keeps each response around 20k tokens — making cost predictable and latency consistently under ~5 seconds.
In short:
Retrieval-Augmented Generation is better suited for ever-changing, information-dense environments like insurance — which is why our AI agent is built on a carefully engineered RAG-architecture, optimized for precision retrieval, cost control, and scalable performance.
Beyond RAG: Finetuning Precision
Choosing to implement a RAG architecture is only the first step in building a production-grade insurance advisor. Real performance depends on precise engineering of the system’s hyperparameters — such as chunking strategies, similarity thresholds, and embedding dimensions.
Insurance documents are uniquely challenging; they contain a high volume of “noise” in the form of legal boilerplate and dense, nested tables that can easily lead to hallucinations if not handled with precision.
Furthermore, insurance queries often require indirect semantic understanding. A user might ask about “key loss,” while the relevant coverage is buried under a clause for “vehicle accessories.” Tuning a system to bridge these linguistic gaps — without letting in irrelevant data — requires strong fine tuning.
At CoverGo, we’ve engineered our RAG pipeline to navigate this complexity, ensuring AI agents don’t just retrieve text but understand intent, context, and policy nuance. This approach underpins CoverGo AI Agents, enabling customer service and operations teams to receive accurate, explainable answers grounded directly in policy language – without manual document navigation.
If you’re looking to deploy AI that delivers accurate, compliant outputs across real insurance workflows, CoverGo brings the architectural rigor and insurance domain expertise required to do it right.
Speak to us about how CoverGo can help your team with AI purpose-built for insurance.
Source:
Academic Reference (CAG Research Paper): Huynh, T. P., & Huang, H. H. (2024). CAG: Cache-Augmented Generation (arXiv:2412.15605). arXiv. https://arxiv.org/abs/2412.15605
Technical Reference (RAG Documentation): OpenAI. (n.d.). Retrieval – OpenAI API. OpenAI Platform. https://platform.openai.com/docs/guides/retrieval
TL:DR
For insurance providers, the choice between RAG and CAG isn’t just technical — it’s about accuracy and scale. While CAG works for small tasks, RAG is the industry standard for handling complex, evolving policy data with high precision and lower costs.
FAQs
While CAG works for small, static datasets, RAG is the industry standard for insurance because it handles massive, evolving policy documentation with 80-90% better cost efficiency and significantly higher accuracy by retrieving only the most relevant context for each query.
Insurance documents contain dense legal boilerplate and nested tables that can cause standard AI to hallucinate. Precision engineering — including fine-tuning chunking strategies and similarity thresholds — ensures the AI understands specific intent and policy nuances rather than just matching keywords.
