# 07 - Managing LLM Context: Memory Allocation and Optimization

Complete guide to Managing LLM Context: Memory Allocation and Optimization: architecture, practical implementation and best practices for developers and technical teams.

## What you'll learn

- Always count tokens before sending with tiktoken or equivalent
- Structure context to mitigate "Lost in the Middle": critical info at start and end
- Use semantic compression instead of brute truncation
- ConversationSummaryBufferMemory is the best choice for long conversations
- Monitor cost-per-query in production and set alerts

*This article is part of the **AI Engineering** series on federicocalo.dev.*

---

## Read the full article

The complete article (15 min read) with code examples, diagrams, and practical exercises is available here:

**➡️ [07 - Managing LLM Context: Memory Allocation and Optimization](https://federicocalo.dev/en/blog/managing-llm-context-memory-allocation-optimization)**

`https://federicocalo.dev/en/blog/managing-llm-context-memory-allocation-optimization`

---

*By [Federico Calò](https://federicocalo.dev) — Software Developer & Technical Writer*