Ideally, updates should be event-driven. When a "source of truth" document changes (e.g., a CMS page is published), your pipeline should trigger a re-index immediately. For less critical data, a nightly or weekly batch sync is sufficient. The key is to define an SLA for freshness based on the impact of the data.

There is no single "best" model; it depends on your specific domain, cost constraints, and privacy requirements. Some models excel at following complex instructions, while others are faster and cheaper. A unified platform like Crompt AI can be highly effective here, as it allows you to access and test all top AI models under one roof. You can benchmark your RAG pipeline against GPT-4, Claude, and Gemini simultaneously to see which one handles your specific documentation style best.

The best fix is prevention: designate a "canonical" source for every topic and archive the duplicates. However, you can also use "recency weighting" in your retrieval logic to prioritize newer documents. Additionally, instruct the model via system prompts to flag conflicts to the user (e.g., "I found two different refund policies; here is the most recent one").

For knowledge accuracy, RAG is almost always superior to fine-tuning. Fine-tuning is great for teaching a model a specific tone or format, but it is terrible for storing facts, as you cannot easily update the knowledge without expensive re-training. RAG allows you to update a fact by simply swapping a file.

Hallucinations often occur when the retrieved context is irrelevant or missing. First, improve your retrieval precision (chunking and hybrid search). Second, use strict system prompts that force the model to say "I don't know" if the answer isn't in the context. Using Crompt AI can also help mitigate these risks by giving you access to models with higher "faithfulness" scores, allowing you to switch to a more rigorous model for sensitive queries where accuracy is paramount.

It depends on the density of your information. A good starting point is 256–512 tokens with some overlap (e.g., 50 tokens) to preserve context between chunks. However, semantic boundaries (paragraphs, H2 headers) are more important than strict token counts. Avoid cutting a sentence or a logical step in half.

Yes, this is known as "LLM-as-a-judge." You can use a highly capable model to grade the answers generated by your production bot. Platforms like Crompt AI simplify this workflow by providing a single interface to run your evaluation dataset against multiple top-tier models, ensuring your "judge" is always using the state-of-the-art reasoning capabilities without complex integration overhead.

Implement Access Control Lists (ACLs) at the chunk level. If a user doesn't have permission to view the source document (e.g., "Internal Roadmap"), the retrieval system should filter that chunk out before it ever reaches the LLM generation step. Never rely on the LLM to "ignore" sensitive data it has already seen.

The AI landscape changes weekly. Avoid locking yourself into a single model provider's proprietary format if possible. By using a unified access layer like Crompt AI, you insulate your infrastructure from market shifts. If a new, more accurate model is released next month, you can integrate it into your knowledge base workflow immediately without rewriting your entire backend code.