Semantic caches are revolutionizing AI-powered applications by drastically reducing query latency and inference costs while improving response consistency. In this episode, we unpack Chapter 15 of Keith Bourne’s 'Unlocking Data with Generative AI and RAG' to explore how semantic caching works, why it’s critical now, and what it means for business leaders scaling AI.

In this episode:

- What semantic caches are and how they optimize AI workflows

- The business impact: slashing response times and inference costs by up to 100x

- Key technical components: vector embeddings, entity masking, and cross-encoder verification

- Real-world use cases across customer support, finance, and e-commerce

- Risks and best practices for tuning semantic caches to avoid false positives

- A practical decision framework for leaders balancing speed, accuracy, and cost

Key tools and technologies mentioned:

- Vector databases (ChromaDB)

- Sentence-transformer models

- Cross-encoder verification models

- Adaptive thresholding and cache auto-population

Timestamps:

0:00 – Introduction and overview of semantic caches

3:30 – Why semantic caches matter now: cost and latency challenges

6:45 – How semantic caches work: embeddings and entity masking

10:15 – Cross-encoder verification and precision vs. speed trade-offs

13:00 – Business payoff: latency reduction and cost savings

16:00 – Risks, pitfalls, and tuning best practices

18:30 – Real-world applications and industry examples

20:30 – Closing thoughts and next steps

Resources:

- "Unlocking Data with Generative AI and RAG" by Keith Bourne – Search for 'Keith Bourne' on Amazon and grab the 2nd edition

- Memriq AI – Visit https://Memriq.ai for AI tools, content, and resources