Thanks — explaining how I work

I’ll give a concise technical explanation of how this assistant (Rebrain) typically operates: the major components, integrations with LLM providers, prompt/wrapper behavior, data flow, and relevant security/privacy notes. After this summary I’ll ask one quick check question to confirm understanding.

Technical overview — how Rebrain (this assistant) works

1) Frontend UI and interaction layer

The web client (browser) hosts the chat interface, interactive exercises/forms, and controls. User input (messages, form responses, uploads) is sent to the backend via authenticated API calls (HTTPS/REST or websockets for real-time).
The UI embeds structured forms (
) and metadata so the backend/assistant can present guided question/feedback flows and validate responses.

2) Backend orchestration and session state

A backend service manages user sessions, conversation state, exercise state, and routing. It stores transient state (in-memory/session store) and persistent state (user progress, analytics) in databases.
The backend composes prompts, templates, and instructions for the language model. It enforces system-level policies (safety filters, rate limits) and may apply business logic (exercise scoring, content transformations).

3) LLM integration layer

The backend calls one or more LLM providers via their APIs (OpenAI, Anthropic, Google, or a self-hosted model endpoint). Calls include: system prompt, conversation history, and user input; and receive generated assistant text (and structured output when used).
Integration can be implemented as a generic adapter/wrapper that abstracts provider differences (auth, rate limits, response formats) so the platform can switch or multiplex providers.

4) Prompt engineering and wrappers

Rebrain uses prompt templates and instruction wrappers to steer the LLM: system prompts (global behavior), few-shot examples, and per-exercise templates that specify style, length, and stepwise teaching rules.
For interactivity, the backend transforms model outputs into the site’s interactive HTML blocks (forms, feedback sections). It also validates user answers and may issue follow-up prompts.

5) Moderation, safety, and policy enforcement

Outgoing prompts and incoming user content pass through content filters (safety classifiers) to block disallowed requests or PII leakage. The platform can enforce refusal behavior in the prompt layer or via a separate moderation service.

6) Data storage and analytics

Conversations, exercise results, and anonymized telemetry are logged for analytics, improvement, and auditing. Sensitive data handling is governed by retention policies; personally identifiable information is either not stored or is encrypted/hashed depending on policy.

7) Optional components

Local or self-hosted model endpoints: some deployments may host models (e.g., on inference servers) instead of third-party APIs; in that case the LLM adapter points to internal endpoints.
Caching and result synthesis: for repeated prompts or expensive calls, the backend may cache model outputs or synthesize answers from microservices (knowledge bases, retrieval-augmented generation).

8) End-to-end data flow (summary)

User types message in UI → sent to backend (HTTPS/websocket).
Backend updates session state and applies policy checks.
Backend constructs prompt (system + history + user input + templates) and sends request to LLM adapter.
LLM provider returns generated response → backend post-processes (safety, formatting) → transforms into interactive HTML blocks and sends to client.
Conversation and telemetry are logged according to retention and privacy settings.

Security & privacy notes

Models: if third-party APIs are used, prompts and user text are transmitted to those vendors (consider data residency and vendor policies).
PII: platforms should avoid sending sensitive PII to external APIs or should apply redaction/encryption policies.
Access control: only authenticated sessions can access user-specific data; tokens and secrets are stored securely on the backend.

Limitations and uncertainty

I can only describe plausible, common architectures and how this assistant behaves; exact proprietary details (internal codebase, vendor agreements) aren’t accessible to me unless disclosed by the platform.

Quick check — which statement best matches how I produce replies?

I generate replies by composing prompts/templates and calling an LLM provider (with backend orchestration)
I run fully in the browser without any backend
I directly access your computer files

Optional: one short sentence why you picked it

Learning: What is this