Context Engineering Philosophy

Fazil is not just an AI application, it's a Retrieval Augmented Generation (RAG) System designed with a singular priority: reliability and context sovereignty. Our architecture focuses on a processing flow that actively reduces the risk of hallucinations and false positives, a crucial challenge in deploying Large Language Models (LLMs) in sensitive and proprietary data environments.

I. Prioritization of Quality and Context Filtering

The foundation of our architecture is the rigorous selection, maintenance and creation of context. We understand that an excess of irrelevant information degrades the LLM's response (the phenomenon of Context Window Crowding). To achieve maximum precision, we have implemented:

Dynamic Filtering Techniques and Metadata Extraction

We use specialized agents to analyze the complete document, extracting and categorizing only the metadata and key information. This intelligent process ensures that the LLM only receives data directly relevant to the user's query, without overloading the context window with unnecessary noise.

Context Synthesis and Summarization Systems

Before sending the retrieved information to the LLM, we apply a high-level synthesis layer. This consolidates data from multiple sources into a concise, high-density summary, optimizing token consumption and refining the precision of the payload that impacts the final response.

II. Prompt Engineering Design for Reliability

The heart of Fazil's interaction is based on Prompt Engineering designed for verifiable precision, not creativity.

Consistent Metaprompting

We use metaprompts to establish a rigid agent role for the LLM, forcing it to base its responses exclusively on the context supplied by the user and to reject any foreign inference or information.

Persistent Memory Management

We have designed memory solutions that allow the AI to remember past interactions and the knowledge base that grows with the user, managing the conversation without having to reload the complete history, making the process faster and more cost-effective.

III. Local-First Architecture and Total Flow Control

We have designed Fazil with a mindset of control and auditability, crucial for trust in AI.

Data Sovereignty

Being a Local-First application, document processing is performed on the user's machine, demonstrating an architectural commitment that guarantees privacy and adherence to regulations such as GDPR and HIPAA.

Context Transparency

The user has full visibility of the context that is about to be sent to the LLM, and the ability to intervene or edit it. This not only improves the experience, but establishes a human validation loop that elevates the final quality of the context used.