Better RAG: a truthful recall layer
Most “RAG” stacks stop at chunks in a prompt. Answers drift, provenance is fuzzy, and the model still fills gaps from weights. Better RAG means a governed memory plane: your facts as versioned nodes and embeddings, injected at inference—with scope, lineage, and checkpoints when your corpus changes.
What “better” changes
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Stable recall, not vibes
Retrieval is tied to explicit workspace memory—not opaque re-ranking that changes run to run without a record.
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Provenance by default
Claims trace to what was ingested, not just “similar text.” That is the bar for review, compliance, and customer trust.
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Same LLM vendor
Amnesis does not replace your model provider. It sits beside any LLM you run and feeds it governed context.
How Amnesis delivers Better RAG
You ingest documents and structured sources into a workspace-scoped plane. The system builds nodes with canonical bodies and projections suitable for search and injection. At chat or query time, recalled material is scoped (workspace, checkpoint policy as you enable it) so “what the system knew” is answerable afterward.
Optional flows such as reflection and checkpoints (from the Amnesis platform and Memory OS capability roadmap) make semantic state and versioning explicit—so Better RAG can deepen alongside that platform depth, not as a one-off pipeline.
Who it is for
Teams that have outgrown demo RAG: regulated industries, internal copilots, customer support, and any workflow where “the model said so” is not an acceptable audit trail. See also industry solutions and other Amnesis use cases.
Try it
Use View Demo for a step-by-step walkthrough, or open Try Better RAG for the live product UI.