# Context Engineering and Long Context System Design Training > Source: https://sukruyusufkaya.com/en/training/context-engineering-ve-long-context-sistem-tasarimi-egitimi > Updated: 2026-06-12T08:21:02.581Z > Level: advanced > Topics: Context Engineering, Long Context, Context Assembly, Context Windows, Retrieval, Memory Systems, Summarization, Compaction, Prompt Caching, Conversation State, Token Budgeting, Long Running Agents, Context Truncation, Hierarchical Context, Session Memory, Agentic Workflows, Evaluation, Observability, AI Architecture, Enterprise AI **TLDR:** An advanced context engineering and long context training for enterprises covering context assembly, retrieval, memory, compaction, summarization, prompt caching, evaluation, and production operations together. ## Açıklama Context Engineering and Long Context System Design Training is an advanced and intensive program designed to help organizations build AI systems not by relying on large context windows alone, but by combining the right information selection, context assembly, retrieval, memory, compaction, caching, evaluation, and production operations. The training positions context engineering not merely as prompt improvement, but as an enterprise engineering discipline that determines what information should be given to a model, in what order, in what format, for how long, and under which cost constraints. Throughout the program, participants systematically learn when long context genuinely creates advantage, when simply passing very large contexts can degrade quality instead of improving it, and how to reason about retrieval, working memory, persistent memory, session state, context assembly, truncation, summarization, compaction, prompt caching, query decomposition, context shaping, tool-augmented context, hierarchical context, memory write/read policies, context budget planning, latency and cost control, observability, evaluation, and governance. The program also examines in detail how context in modern agent and assistant systems is not just chat history, but a layered structure made up of system instructions, tool schemas, prior steps, external data sources, summaries, intermediate outputs, and user state. This training addresses several critical needs: organizations often treat large context windows as if they were the full solution; they cannot clearly define retrieval and memory strategies; they experience quality degradation, latency growth, and cost spikes as conversations grow over time; they cannot systematize which context component should be used when in long-document workflows, multi-file processes, agentic workflows, coding, reporting, research, and enterprise assistants; and they want to turn context engineering from experimental prompt adjustments into a production-grade architectural discipline. The program focuses exactly on these needs and provides the technical framework that makes long-context systems more defensible, higher quality, and more sustainable at enterprise scale. A major differentiator of the program is that it does not treat long context merely as the ability to pass more tokens. Participants see that strong long-context systems must make conscious decisions about what information should be included, what should be summarized, what should be retrieved on demand, what should be written into memory, and what should be excluded from context. For that reason, the training goes beyond writing longer prompts and focuses instead on building context architectures that are more intelligent, more cost-efficient, and more governable. By the end of the training, participants gain a more mature engineering perspective that enables them to analyze context engineering needs according to the use case, balance long context with retrieval and memory correctly, design context assembly and budget management, systematize compaction and summarization strategies, manage the balance of quality, cost, and latency more effectively, and move long-context AI systems from prototype to enterprise production. ## Kazanımlar - Analyze context engineering needs according to the use case. - Balance long context with retrieval and memory correctly. - Design context assembly and budget management. - Systematize compaction and summarization strategies. - Manage the balance of quality, cost, and latency more effectively. - Develop a more mature engineering approach for moving long-context AI systems from prototype to enterprise production.

Detailed Content (EN)

This training is designed for technical teams that want to build enterprise AI systems more deliberately when working with models that support long context windows. At the center of the program is one core idea: strong AI systems succeed not by giving the model as much data as possible, but by giving the right data at the right time, in the right form, and within the right cost boundaries. For that reason, context engineering goes beyond prompt writing and becomes a production-oriented system design approach that combines information selection, information organization, context flow, retrieval, memory, compaction, summarization, caching, observability, and quality assurance.

Throughout the training, participants learn to evaluate long context not as a complete solution in itself, but as part of a broader system architecture. Large context windows can offer major advantages in some use cases; however, as context grows, risks such as quality degradation, attention dilution, unnecessary information load, latency, and cost also increase. For that reason, the program is not about sending more tokens, but about managing context better. This allows teams to design more sustainable systems by thinking about long context, retrieval, and memory together.

One of the strongest aspects of the program is that it treats context not as a single layer, but as a multi-layer structure. Participants see that system instructions, role definitions, tool schemas, prior steps, user state, temporary working notes, document summaries, retrieval results, and persistent memory records each serve different purposes. In this way, the context window stops being just a place that stores conversation history and becomes the central orchestration surface for AI systems that reason, use tools, and preserve state.

A second major axis is context assembly and budget management. Participants systematically learn which data should be included when, which data should be retrieved on demand instead of being injected directly into long context, which data should be summarized or compressed, and which data should be excluded entirely. In this context, topics such as context budgets, token planning, truncation, summarization, compaction, selective inclusion, recency prioritization, and importance-based filtering are covered in depth. This turns long-context systems from randomly growing prompts into consciously managed information flows.

The program also explores memory and long-running interactions in detail. Participants learn that working memory, session summaries, persistent memory, user preferences, state transfer, and task handoff are different layers, each requiring different storage, recall, and update strategies. This makes problems such as context loss, premature wrap-up behavior, repeated information load, and quality decay more manageable in long tasks and agentic workflows.

Another strong dimension is evaluation and observability. Participants see that the quality of context engineering should not be measured only through model answers, but also through signals such as the quality of included information, retrieval accuracy, summary adequacy, semantic loss after compaction, caching effects, token cost, latency, context overflow risk, and failure visibility. This transforms long-context systems from working demos into measurable production services in terms of quality, cost, and reliability.

The final major focus is governance, security, and production rollout. Participants address topics such as how much sensitive data should enter context, permission-aware retrieval, secure memory writes, audit trails, versioned prompt and context templates, rollout strategies, rollback, maintenance, and capability roadmaps. In this way, context engineering becomes not merely a technique for improving model quality, but an architectural discipline that enables enterprise control, security, and sustainable operations.