https://github.com/lemessaA/rt-aaidc-project1.git
Retrieval-Augmented Generation (RAG) systems combine information retrieval with large language models to produce context-aware, accurate responses. While a basic RAG pipeline works, it usually behaves like a goldfish. No memory, no accountability, and no idea what went wrong when it fails.
This document explains how to enhance a RAG system by:
. Adding basic logging and observability to understand system behavior
. Introducing session-based memory to maintain conversational context across interactions
These enhancements improve reliability, debuggability, and user experience without turning the system into an overengineered nightmare.
System Architecture
The enhanced RAG system consists of four core components:
Query Handler
Retrieval Engine
Language Model Generator
Session and Observability Layer
The Session and OLogging and Observability Design
Basic logging is implemented at each stage of the RAG pipeline. Logs capture:
User queries and session identifiers
Retrieved document identifiers and similarity scores
Generation latency and token usage
System errors and exceptions
Observability metrics are derived from logs and include response time, retrieval success rate, and error frequency. Request-level tracing is used to follow a single query through retrieval and generation phases.bservability Layer is introduced to manage conversational state and collect system-level telemetry. Session-Based Memory Integration
Session-based memory is scoped per user session and persists only during active interaction. The memory stores:
Previous user queries
Model-generated responses
Retrieved document references
This memory is injected into both the retrieval phase, by augmenting search queries, and the generation phase, by enriching the prompt context sent to the language model.
Experiments were conducted to evaluate the impact of logging, observability, and session-based memory on system performance and response quality.
Two system configurations were compared:
Baseline RAG without logging or memory
Enhanced RAG with logging, observability, and session-based memory
Test scenarios included single-turn queries, multi-turn conversations, and ambiguous follow-up questions. Performance metrics and qualitative response relevance were recorded across multiple sessions.
The enhanced RAG system demonstrated measurable improvements across all evaluation dimensions. Session-based memory significantly improved the relevance of responses in multi-turn interactions. Follow-up questions showed higher contextual accuracy compared to the baseline system.
Logging and observability enabled precise identification of retrieval failures and latency bottlenecks. Error diagnosis time was reduced, and system behavior became more transparent during evaluation.
Overall, the enhanced system produced more coherent, consistent, and traceable responses.
This study demonstrates that incorporating basic logging, observability, and session-based memory substantially improves the effectiveness of RAG systems. Logging and observability provide essential insight into system operations, while session-based memory enables contextual continuity across interactions.
These enhancements require minimal architectural changes yet deliver significant gains in reliability, maintainability, and user experience. Future work may explore long-term memory strategies and adaptive retrieval optimization based on observed session behavior.
