> ## Documentation Index > Fetch the complete documentation index at: https://kremis.mintlify.site/llms.txt > Use this file to discover all available pages before exploring further. # Design Philosophy > Why Kremis is built the way it is. This page explains the reasoning behind Kremis's design constraints. Not what Kremis does — but why it does it that way. ## The Problem Hallucination in AI is a structural problem, not an accuracy problem. Large language models fuse communication and knowledge storage into the same probabilistic system. Given a question, they generate a plausible-sounding answer by interpolating across their training data — whether or not the specific fact exists. The problem is not that the model is "wrong sometimes." The problem is that the model *cannot distinguish between what it knows and what it is constructing on the fly.* Improving accuracy (more parameters, more RLHF, better prompting) reduces hallucination rate. It does not eliminate the structural ambiguity between fact and fabrication. A system that hallucinates 0.1% of the time and a system that hallucinates 10% of the time share the same architecture: one where Truth and Intelligence are intertwined. ## The Insight The solution is architectural: separate Truth from Intelligence. Instead of asking a model to "remember" facts, keep facts in a deterministic graph store where every piece of data traces back to a real ingested signal. The graph either contains a connection or it does not. There are no probabilities, no weights, no interpolation. An intelligent layer (an LLM, a query engine, an application) then operates *above* this grounded substrate. Its outputs are constrained by what the graph actually contains. If the graph has no path supporting a claim, the system does not guess — it returns nothing. This separation is the founding principle of Kremis. ## What Kremis Is Not The system does not *understand*. It contains only the structure of the signals it has processed. The initial graph is completely empty. All structure emerges exclusively from ingested signals. Kremis is deliberately not: * **A learning system** — the graph does not self-modify based on inference. Data is added explicitly through ingest. There is no weight update, no latent adaptation. * **A generative AI** — Kremis does not produce text, complete prompts, or generate answers. It stores and retrieves structural relationships. * **An optimizer or planner** — the core has no goals. It reacts to signals and queries. It does not decide, prioritize, or strategize. * **A chatbot or natural language processor** — natural language understanding happens outside the core, in the application layer that calls Kremis APIs. * **An approximation engine** — if a traversal targets a missing node or edge, the result is `None`. The system does not fill gaps, complete paths, or return "close enough" answers. These are not current limitations to be lifted in future versions. They are permanent design constraints. Expanding the core beyond this scope would define a different project. ## The 4 Laws as Design Consequences The four laws that govern Kremis's codebase follow directly from the architectural separation above. Each law exists because violating it would reintroduce the structural ambiguity that Kremis is designed to eliminate. **1. Determinism** — Same input, same output, every time. If two runs of the same query on the same graph produce different results, the system cannot be trusted as a source of truth. Non-determinism at the core level — randomness, hash-map ordering, floating-point arithmetic — would make the graph unreliable as a verification substrate. `BTreeMap` everywhere, no `HashMap`, integer arithmetic only, no timestamps in core logic. **2. Precision** — Every response is a Fact, an Inference, or "unknown." Nothing else. Honest output means the system never silently fills gaps. A missing edge returns `None`. No `todo!()`, no `unimplemented!()`, no internal assumptions that bypass the graph. If the information is not in the graph, the caller receives an explicit `"unknown"` grounding — not a default, not a guess. **3. Security** — The substrate of truth must itself be safe. A grounded system is only valuable if it cannot be corrupted. Constant-time authentication prevents timing attacks on API keys. Input validation and path traversal protection prevent malformed signals from poisoning the graph. Rate limiting prevents the substrate from being overwhelmed. No `.unwrap()`, no `panic!()` in core logic — all errors are recoverable and explicit. **4. Separation** — kremis-core is pure; I/O happens in the application layer. The codebase enforces the architectural separation in code. `kremis-core` has no async, no network, no I/O. It is a deterministic library. The HTTP server, CLI, and MCP bridge live in separate crates that call into the core. This boundary ensures the core cannot be contaminated by non-deterministic runtime concerns, and that the core can be tested and verified in isolation. See [CONTRIBUTING.md](https://github.com/TyKolt/kremis/blob/main/.github/CONTRIBUTING.md) for the complete engineering rules derived from these four laws. ## Honest Intelligence A system is honest when it can say "I don't know" as reliably as it can say "I know." Most AI systems are optimized to produce an answer — any answer. Kremis is optimized for the opposite: to produce *no* answer when the supporting structure is absent. An empty graph returns nothing. A graph with three signals returns exactly what those three signals imply, no more. The result is a substrate that an intelligent layer can trust unconditionally. When Kremis says a relationship exists, it exists — because a real signal created it. When Kremis returns `None`, no fabrication has occurred. The downstream application can treat every response as a hard fact or a hard absence, not as a probability. This is what "Honest Intelligence" means in the context of Kremis: not a system that is honest about its limitations in a disclaimer, but a system whose architecture makes dishonesty structurally impossible.