Mphasis

Walk through orchestrating a 500M-vector re-embedding backfill: rate limits, checkpoints, verification, and cutover readiness.

Estimate the parameters weight tying saves a 128k-vocab model at d_model 2048

Justify why hybrid SSM models like Jamba still keep a slice of attention layers

Pick the routing call for a password reset question hitting your support bot

Fill in the s1 budget forcing trick that stretches or stops thinking

Which metrics isolate the retriever's quality, separate from how good the final answer reads?

Pick the situations where GraphRAG earns its build cost over plain vector RAG

Cutting the number of vision tokens a VLM sends to the LLM without wrecking accuracy

Contrast canary rollout with shadow mode for a prompt deployment

Explain what gen_ai.response.finish_reasons captures and why it is a list