Chapter 2 -- The 50-Day Plan For Building A Personal Assistant Agentic System (PAAS)

PHASE 4: SYSTEM INTEGRATION & POLISH (Days 41-50)

Day 44-46: Advanced Email Capabilities

These three days focus on enhancing your PAAS's email capabilities, enabling more sophisticated outreach and intelligence gathering through email communications. You'll study advanced techniques for natural language email generation that creates personalized, contextually appropriate messages. You'll develop systems for analyzing responses to better understand the interests and expertise of your contacts. You'll create smart follow-up scheduling that maintains relationships without being intrusive. Throughout, you'll implement these capabilities with a focus on security, privacy, and efficient processing using Rust and LLMs in combination.

• Morning (3h): Study advanced email interaction patterns with Rust/LLM combination

  • Natural language email generation: Learn techniques for generating contextually appropriate emails that sound natural and personalized rather than automated or generic. Develop prompt engineering approaches for guiding LLMs to produce effective emails, using Rust to manage templating, personalization variables, and LLM integration with strong type safety.
  • Response classification: Study methods for analyzing email responses to understand sentiment, interest level, questions, and action items requiring follow-up. Implement a Rust-based pipeline for email processing that extracts key information and intents from responses, using efficient text parsing combined with targeted LLM analysis for complex understanding.
  • Follow-up scheduling: Explore strategies for determining optimal timing and content for follow-up messages, balancing persistence with respect for the recipient's time and attention. Create scheduling algorithms in Rust that consider response patterns, timing factors, and relationship history to generate appropriate follow-up plans.

• Afternoon (3h): Enhance email system with Rust performance

  • Implement contextual email generation: Build a sophisticated email generation system that creates highly personalized outreach based on recipient research interests, recent publications, and relationship history. Develop a hybrid approach using Rust for efficient context assembly and personalization logic with LLMs for natural language generation, creating a pipeline that can produce dozens of personalized emails efficiently.
  • Build response analysis system: Create an advanced email analysis component that can extract key information from responses, classify them by type and intent, and update contact profiles accordingly. Implement named entity recognition in Rust to identify people, organizations, and research topics mentioned in emails, building a knowledge graph of connections and interests over time.
  • Create autonomous follow-up scheduling: Develop an intelligent follow-up system that can plan email sequences based on recipient responses, non-responses, and changing contexts. Implement this system in Rust for reliability and performance, with sophisticated scheduling logic that respects working hours, avoids holiday periods, and adapts timing based on previous interaction patterns.