Essential Insights
- Beyond Prompt Caching, additional caching strategies—such as query embedding, retrieval, reranking, prompt assembly, and query-response caches—can significantly reduce latency and costs in AI applications.
- Exact-match caches (e.g., Redis) work well for identical queries, while semantic caches (e.g., vector databases like ChromaDB) handle semantically similar queries, offering more flexible reuse.
- Different cache types often have distinct expiration policies, making their independent management crucial to maintaining updated and relevant results in dynamic knowledge bases.
- Combining multiple caching layers in a RAG pipeline optimizes performance, enabling high-traffic AI apps to operate more efficiently while minimizing redundant computations.
Beyond Prompt Caching: 5 More Things You Should Cache in RAG Pipelines
As artificial intelligence becomes more advanced, developers find new ways to save time and money. One such method is caching. We’ve already seen how prompt caching helps with large language models (LLMs). Now, let’s explore five other parts of AI systems where caching can make a big difference.
Why Is Caching Important?
Caching works because many user queries are similar or repeated. For example, employees often ask similar questions like “How many days of leave do I have?” or “What’s the process for expenses?” Even if wording differs, these queries are semantically alike. So, caching these similar questions saves processing time and reduces costs.
1. Query Embedding Cache
When a user asks a question, the system turns it into a vector called an embedding. Generating this embedding each time can slow things down. Instead, we can store embeddings for repeated queries. If a question appears again, the system reuses the previous embedding. This way, responses are quicker, and resources are saved. For example, “What are Athens’ area codes?” might be stored and reused later.
2. Retrieval Cache
Next, the retrieval step can also benefit from caching. Once a question is asked, relevant documents or chunks are retrieved. If the same or similar question is asked again, the system can fetch these chunks from the cache. This avoids repeating the full search process. For instance, if someone asks about travel policies, the system can reuse results from earlier similar questions.
3. Reranking Cache
Sometimes, retrieved documents are evaluated and ordered by a reranker. Caching this order helps if the same question and document set come up later. For example, if the system previously ranked certain chunks highly for a question about Athens, it can reuse that ranking. This cuts down on reranking time and keeps the system efficient.
4. Prompt Assembly Cache
Creating the final prompt involves putting together retrieved chunks, system instructions, and the user’s question. If this exact setup appears again, caching can provide the preassembled prompt. This reduces processing time, especially when prompt construction is complex, speeding up responses for frequent questions.
5. Query-Response Cache
Finally, the most straightforward cache stores complete questions and answers. When the same question comes up, the system instantly provides the cached response. This method completely bypasses retrieval and generation, offering near-instant answers. It is especially helpful for common or repetitive questions.
Many applications combine these caching strategies. Using multiple caches together improves overall speed and reduces costs. As AI continues to grow, these caching techniques will become even more vital for efficient, user-friendly systems.
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