RAG Isn’t Enough — I Built the Missing Context Layer That Makes LLM Systems Work

2026-04-14 · Source: Towards Data Science · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, long

Summary

A pure Python implementation of a "context engineering" pipeline is presented, designed to manage and optimize the information flow into Large Language Model (LLM) context windows for RAG systems. This architecture addresses common RAG failures in multi-turn conversations, such as context overflow, irrelevant document inclusion, and forgetting, by explicitly controlling memory, compression, re-ranking, and token limits. The system integrates a hybrid retriever combining keyword, TF-IDF, and dense vector embeddings, a re-ranker with tag-based importance, an exponential decay memory system for conversational history, and a context compressor with extractive capabilities. Benchmarks on a CPU-only setup show the full engine's build latency at approximately 92ms, with hybrid retrieval being the primary bottleneck at ~85ms.

Key takeaway

For AI Engineers building multi-turn RAG systems or AI copilots, implementing a dedicated context engineering layer is crucial. Your system will adapt to token pressure by intelligently compressing and prioritizing context, rather than failing due to overflow or irrelevant information. Consider integrating hybrid retrieval, exponential memory decay, and a token budget enforcer to ensure coherent and efficient LLM interactions, especially in production environments with real-world constraints.

Key insights

Context engineering explicitly manages information flow into LLM context windows to prevent RAG system failures.

Principles

• Hybrid retrieval improves relevance over single methods.
• Exponential decay memory prevents context bloat.
• Token budget enforcement requires explicit ordering.

Method

The pipeline orchestrates hybrid retrieval, re-ranking, exponential decay memory, and query-aware compression, reserving token budget for system prompts, memory, and then retrieved documents in that order.

In practice

• Implement hybrid retrieval with tunable alpha weighting.
• Use exponential decay for conversational memory.
• Prioritize system prompt and memory in token allocation.

Topics

• Context Engineering
• RAG Systems
• Hybrid Retrieval
• Memory Management
• Token Budget Control

Code references

• Emmimal/context-engine
• openai/tiktoken

Best for: AI Engineer, Machine Learning Engineer, MLOps Engineer

Related on AIssential

• Long Context Is Not Replacing RAG - It Is Forcing Us to Rethink Context Engineering — Long context models enhance, rather than replace, RAG, demanding advanced context engineering.
• Solving AI Amnesia at Scale: Context Pipelines for Large Enterprises — LLMs need scalable context pipelines with dynamic routing to overcome "amnesia" in enterprise applications.
• The Context Window Trap: Stop Drowning Your AI in Data — Overloading LLM context windows with data degrades performance, increases cost, and erodes engineering craft.
• A Guide to Context Engineering for LLMs — More context can degrade LLM performance due to architectural limitations and uneven attention distribution.
• Why Most AI Systems Fail (And It’s Not the Model) — AI system failures often stem from poor context retrieval, not the LLM, due to inadequate data architecture.
• Why LLMs Fail and How RAG Makes AI Responses Smarter and More Reliable — RAG grounds LLM responses in external data, preventing hallucinations and ensuring up-to-date, accurate answers.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Towards Data Science.