Context Engineering Guide: RAG, Memory Systems & Enterprise Knowledge Bases

1. From Prompt Engineering to Context Engineering: A Paradigm Shift

Over the past three years, Prompt Engineering has been the core skill for enterprises interacting with Large Language Models (LLMs). Through carefully designed prompts, developers can significantly improve output quality without modifying model weights. However, as AI applications evolve from simple Q&A chatbots to complex multi-step workflows and autonomous agents, a fundamental problem has emerged: simply "writing good prompts" is far from enough.

Context Engineering is a systematic methodology born precisely to address this challenge. It focuses not just on the prompt itself, but on ensuring the model has all the context needed to complete a task at the moment of LLM inference — including the right knowledge, relevant history, appropriate tool descriptions, and structured instructions. If Prompt Engineering is "writing a good letter," then Context Engineering is "building the entire postal system."

Gao et al. noted in their 2024 RAG survey^[2] that over 70% of errors in modern LLM applications stem not from insufficient model capability but from incomplete, irrelevant, or poorly structured context. This data reveals a counterintuitive truth: in 2026, when model capabilities are already sufficiently powerful, the critical bottleneck determining the success or failure of AI applications has shifted from the "model side" to the "context side."

1.1 Core Components of Context Engineering

A complete Context Engineering system comprises four pillars:

• Knowledge Retrieval Layer: Real-time retrieval of relevant information from external knowledge bases through RAG, GraphRAG, and other technologies
• Memory Management Layer: Managing conversation history, user preferences, and long-term cross-session memory
• Context Orchestration Layer: Determining which information is injected into the context window, in what order, and in what format
• Tools & Environment Layer: Providing the LLM with callable tool descriptions, API schemas, and environment state

1.2 Prompt Engineering vs. Context Engineering

2. RAG Architecture Deep Dive: From Basics to Advanced

Retrieval-Augmented Generation (RAG) is the most critical technical component of Context Engineering. Since Lewis et al. introduced the RAG concept in 2020^[1], this technology has evolved from a prototype in academic papers to a standard architecture for enterprise AI applications. However, truly understanding the full landscape of RAG — from Naive RAG to Advanced RAG to Agentic RAG — is essential for building enterprise-grade systems.

2.1 Three Generations of RAG Evolution

First Generation: Naive RAG (2020-2023). The most basic RAG implementation follows a linear "index-retrieve-generate" pipeline. Documents are split into fixed-length chunks, converted to vectors via embedding models, and stored in vector databases^[10]. At query time, the system retrieves the top-k chunks by semantic similarity and directly concatenates them as the LLM's context. This approach is simple and intuitive but faces three core problems: semantic fragmentation during chunking, insufficient retrieval precision, and lack of quality validation for retrieved results.

Second Generation: Advanced RAG (2023-2025). To address Naive RAG's problems, the community developed a series of enhancement techniques: query rewriting, hybrid search (combining semantic and keyword search), re-ranking, parent-child chunking, and self-query (letting the LLM decide the retrieval strategy). Frameworks like LlamaIndex^[6] and LangChain^[5] packaged these techniques into composable modules, significantly lowering the barrier to enterprise adoption.

Third Generation: Agentic RAG (2025-Present). Agentic RAG represents a fundamental leap — it upgrades RAG from a passive "retrieval pipeline" to an active "intelligent agent." RAG agents can: autonomously determine whether retrieval is needed, dynamically select retrieval sources (vector stores, knowledge graphs, web search, APIs), evaluate retrieval result quality and decide whether re-retrieval is needed, and self-verify answer correctness after generation. This architecture draws on the self-reflection mechanism from Shinn et al.'s Reflexion framework^[8], giving RAG systems self-correction capabilities.

2.2 Three-Generation RAG Architecture Comparison

2.3 Agentic RAG Architecture Patterns

The core of Agentic RAG lies in using the LLM as a "reasoning engine" to drive the entire retrieval-generation flow. Below is a typical Agentic RAG decision flow:

Agentic RAG Decision Flow:

User Query → LLM Routing Decision
  │
  ├─ Decision 1: Is external knowledge needed?
  │   ├─ No → Answer directly with model's built-in knowledge
  │   └─ Yes → Enter retrieval phase
  │
  ├─ Decision 2: Select retrieval source
  │   ├─ Factual query → Vector database (Pinecone/Weaviate)
  │   ├─ Relational reasoning → Knowledge graph (Neo4j GraphRAG)
  │   ├─ Real-time information → Web Search API
  │   └─ Structured data → SQL / API query
  │
  ├─ Decision 3: Are retrieval results sufficient?
  │   ├─ No → Query rewrite / expansion → Secondary retrieval
  │   └─ Yes → Enter generation phase
  │
  └─ Decision 4: Post-generation self-verification
      ├─ Answer consistent with sources → Return to user
      └─ Contradiction detected → Trigger Reflexion → Re-retrieve

3. Context Window Management: Strategies for the 200K+ Token Era

Between 2025 and 2026, the context windows of mainstream LLMs experienced explosive growth: Claude supports 200K tokens^[3], Gemini 3 Pro reaches 2 million tokens^[4], and GPT-4.5 provides 256K tokens. This ultra-long context capability seemingly eliminates the need for RAG — why not just stuff all documents into the context window? However, practical experience tells us that ultra-long context brings not only opportunities but entirely new engineering challenges.

3.1 The "Lost in the Middle" Problem

Research shows that LLMs exhibit clearly uneven attention distribution when processing ultra-long contexts — the model pays significantly more attention to the beginning and end of the text than to the middle. Anthropic explicitly advises in its long context usage guide^[3] that the most critical information should be placed at the beginning or end of the context to avoid important content being "buried" in the middle. This phenomenon means that even when the context window is large enough, the arrangement order of information remains crucial.

3.2 Context Window Optimization Strategies

Effective context window management requires balancing "information completeness" with "attention distribution." Here are four practice-validated strategies:

Strategy 1: Hierarchical Context Structure. Rather than injecting all content as a flat list into the context, build a hierarchical structure: the top layer holds the system prompt and task definition; the second layer holds the most directly relevant retrieval results (after re-ranking); the third layer holds supplementary background knowledge; the bottom layer holds tool descriptions and format instructions. This structure allows the model to "see the most important information first."

Strategy 2: Dynamic Context Compression. When information volume exceeds context window limits, use an LLM or specialized compression model to summarize lower-priority context. For example, earlier messages in conversation history can be compressed into summaries; long retrieved documents can be distilled into key passages. This approach preserves information semantics while saving precious token space.

Strategy 3: Selective Injection. Not all context needs to be present in the context window simultaneously. Through LLM-driven routing logic, the system can dynamically decide which knowledge fragments to inject based on the nature of the current query. For instance, when the user asks a financial question, the system injects financial documents and conversation history; when the topic shifts to technical issues, it dynamically swaps in technical documentation.

Strategy 4: Structured Tagging. Use explicit XML or Markdown tags in injected context to distinguish information from different sources and types. For example:

<context>
  <system_instructions>
    You are a financial regulatory advisor...
  </system_instructions>

  <retrieved_knowledge source="regulatory_database" relevance="0.94">
    Financial Supervisory Commission 2025 Bulletin No. 42: Regarding virtual assets...
  </retrieved_knowledge>

  <conversation_history compressed="true">
    [Summary] The user previously asked about the basic framework of cryptocurrency regulation...
  </conversation_history>

  <tool_definitions>
    search_regulations: Search the financial regulatory database...
    calculate_penalty: Calculate regulatory violation penalty amounts...
  </tool_definitions>
</context>

3.3 Long Context vs. RAG: When to Use Which?

Ultra-long context windows and RAG are not mutually exclusive technology choices but complementary strategies. Here is a decision framework we have derived from practice:

• Total documents < 50 pages and low update frequency: Put them directly into long context — no need for the additional complexity of RAG architecture
• Total documents 50-500 pages: Use a combination of RAG retrieval + long context — first filter the most relevant passages via RAG, then leverage long context capabilities to process multiple passages simultaneously
• Total documents > 500 pages or continuously growing: RAG is essential (with vector databases or GraphRAG), and long context is used only to hold single-query retrieval results
• Precise source attribution required: RAG is superior to long context — RAG architecture natively supports source tracking, while information sources within long context are harder to trace

4. Memory System Design: Giving AI True "Memory"

Human cognitive ability depends significantly on memory systems — we can recall yesterday's conversation, apply years of accumulated expertise, and maintain short-term memory of current tasks at work. However, standard LLMs are "stateless": every inference starts from scratch, with no memory of prior interactions. The memory management layer of Context Engineering exists precisely to fill this fundamental gap.

4.1 Three-Layer Memory Architecture

In their 2023 Generative Agents research^[9], Park et al. designed a cognitive-science-inspired memory architecture for AI agents. Building on this foundation, we propose a three-layer memory model suitable for enterprise AI systems:

Working Memory. Working memory corresponds to the context of the current conversation. It includes all messages in the current conversation turn, retrieved knowledge fragments, and tool call results. Working memory is constrained by context window size and is the "most expensive" but also "most precise" form of memory. The core challenge of managing working memory is: within a limited token budget, deciding which information to retain, which to compress, and which to discard.

Episodic Memory. Episodic memory stores "experiences" from past interactions — summaries of previous conversations, questions the user has asked, errors the system has made and corrections applied. This type of memory is stored in external databases and injected into working memory through retrieval when needed. Episodic memory enables AI assistants to "remember" user preferences (such as report formats and language styles) and previously established decision context, achieving cross-conversation continuity.

Semantic Memory. Semantic memory corresponds to the system's "knowledge base" — enterprise documents, domain knowledge, policy specifications, and other structured and unstructured knowledge. This is the core layer managed by the RAG system. Semantic memory is the most stable form of memory with the lowest update frequency, but its quality directly determines the upper bound of the AI system's professional capabilities.

4.2 Engineering Practices for Memory Systems

At the engineering level, each memory layer requires corresponding storage and retrieval mechanisms:

Memory System Architecture:

Working Memory
├─ Storage: LLM context window (in-memory)
├─ Capacity: 200K - 2M tokens (model-dependent)
├─ Strategy: Conversation history compression, sliding window, importance-weighted retention
└─ Latency: 0ms (already in context)

Episodic Memory
├─ Storage: Vector database + structured database
├─ Capacity: Unlimited
├─ Strategy: Auto-summarize and archive at conversation end, retrieve and inject by relevance
└─ Latency: 50-200ms (retrieval latency)

Semantic Memory
├─ Storage: Vector database + knowledge graph + file system
├─ Capacity: Unlimited
├─ Strategy: RAG pipeline (chunking → embedding → indexing → retrieval → re-ranking)
└─ Latency: 100-500ms (retrieval + re-ranking latency)

4.3 Reflexion: Memory-Driven Self-Improvement

The Reflexion framework proposed by Shinn et al.^[8] demonstrates one of the most exciting applications of memory systems: enabling AI agents to learn from their own failures. In the Reflexion architecture, after completing a task, the agent self-evaluates the quality of its results; if it detects errors, the agent generates a "reflection" text — analyzing the reasons for failure and strategies for improvement — and stores this reflection in episodic memory. The next time it faces a similar task, the system retrieves relevant reflection records to avoid repeating past mistakes.

This mechanism is extremely valuable in enterprise scenarios. For example, after an AI assistant handling customer complaints incorrectly classifies a complaint, the system automatically records: "Misclassified a return request as a product inquiry — Reason: customer used indirect phrasing without explicitly mentioning returns. Improvement strategy: when the customer mentions dissatisfaction, unmet expectations, or similar terms, prioritize return/refund intent." This memory is automatically retrieved in subsequent similar scenarios, continuously improving the system's classification accuracy.

5. Context Management in Multi-Agent Systems

As AI applications evolve from single agents to multi-agent collaborative systems, Context Engineering faces entirely new challenges: How can multiple agents share information while maintaining their respective focus? How can context contamination be avoided? How should efficient inter-agent communication protocols be designed?

5.1 Multi-Agent Context Architecture Patterns

In multi-agent systems, each agent has its own context window, but they need to collaborate to accomplish shared tasks. We have observed three mainstream context-sharing patterns:

Pattern 1: Shared Blackboard. All agents share a central "blackboard" (typically a structured document or database). Each agent writes its intermediate results to the blackboard and reads other agents' outputs from it. The advantage of this pattern is simplicity and transparency; the drawback is that it easily causes context bloat — every agent sees all information, including content unrelated to its own task.

Pattern 2: Message Passing. Agents communicate through concise messages, each containing only the minimum information needed by the receiving agent. This pattern effectively controls context bloat but requires careful design of message formats and routing logic. LangChain's AI Agent frameworks^[5] adopt this pattern.

Pattern 3: Hierarchical Delegation. A "Supervisor Agent" is responsible for receiving tasks, decomposing them into subtasks, and delegating them to specialized "Worker Agents." The supervisor agent maintains global context, while worker agents receive only the local context relevant to their subtasks. Upon task completion, worker agents report results back to the supervisor agent, which integrates them and produces the final response. This is currently the most mature pattern in enterprise multi-agent systems.

5.2 Agent Context Isolation and Sharing

When designing multi-agent context architecture, the key design decision is: which information should be shared and which should be isolated. We recommend the following principles:

• Shared: Task objectives, global constraints, final output format requirements, shared knowledge base access permissions
• Isolated: Each agent's dedicated system prompt (defining role and behavior), intermediate results of tool calls (unless other agents explicitly need them), each agent's reasoning process (to avoid reasoning interference)
• Selectively Shared: Compressed summaries of intermediate results (rather than raw reasoning processes), error reports and correction records, inter-agent coordination state

Multi-Agent Context Architecture Example:

Supervisor Agent [context: global task + subtask status]
  │
  ├─ Research Agent [context: research instructions + knowledge base access]
  │   ├─ Semantic memory: Enterprise document vector store
  │   └─ Output: Structured research report summary → Supervisor
  │
  ├─ Analysis Agent [context: analysis instructions + Research results]
  │   ├─ Tools: Data analysis API, chart generation
  │   └─ Output: Analysis conclusions + data visualizations → Supervisor
  │
  └─ Writing Agent [context: writing instructions + analysis conclusions]
      ├─ Episodic memory: User style preferences
      └─ Output: Final report → User

6. Enterprise Knowledge Base Construction and Context Optimization Practices

Translating the theoretical architecture above into a system usable by enterprises requires a systematic engineering methodology. This section covers vector database selection, embedding strategies, chunking optimization, and end-to-end quality monitoring to provide a complete enterprise knowledge base construction guide.

6.1 Vector Database Selection

Vector databases are the infrastructure of Context Engineering. Pinecone notes in its enterprise RAG best practices report^[10] that when selecting a vector database, five key dimensions must be considered: query latency, scalability, filtering capabilities, operational cost, and integration with the existing tech stack. Here is a comparison of mainstream solutions:

• Pinecone: Fully managed service with zero operational overhead, suitable for small to mid-sized projects needing rapid launch. Supports metadata filtering and hybrid search, but pricing scales relatively quickly.
• Weaviate: Open-source + cloud dual-mode, with built-in BM25 + vector hybrid search and GraphQL query interface. Suitable for enterprises requiring custom deployments.
• Milvus / Zilliz: High-performance open-source vector database designed for billion-scale vector operations. Suitable for scenarios with massive data volumes and extreme performance requirements.
• Qdrant: Implemented in Rust, with extremely low latency and rich filtering capabilities. Suitable for edge device deployment scenarios.
• pgvector (PostgreSQL Extension): Enables vector search directly within an existing PostgreSQL installation, with no additional infrastructure needed. Suitable for teams already using PostgreSQL with moderate data volumes.

6.2 Embedding Strategies

The choice of embedding model directly impacts retrieval quality. Current industry best practices include:

Choose models optimized specifically for retrieval. General-purpose language model embeddings (such as GPT-4) are not the optimal choice for retrieval. Models designed specifically for semantic search — such as OpenAI text-embedding-3-large, Cohere embed-v4, and BGE-M3 — typically outperform general models by 15-25% on retrieval tasks.

Consider multilingual requirements. For enterprises in Taiwan, knowledge bases typically contain a mix of Traditional Chinese, English, and even Simplified Chinese documents. Choosing a multilingual embedding model (such as BGE-M3 or Cohere embed-v4) is critical; otherwise, cross-language semantic matching will be severely impaired.

Dimensionality vs. quality trade-off. Higher-dimensional embeddings (such as 3072 dimensions) generally have richer semantic representation capabilities but also incur higher storage costs and query latency. For most enterprise scenarios, 1024-dimensional embeddings provide sufficient retrieval quality and represent the cost-effectiveness sweet spot.

6.3 Intelligent Chunking Strategies

Document chunking is one of the most significant determinants of RAG quality, yet it is often underestimated. Here are practice-validated chunking principles:

• Prioritize semantic boundaries: Split at paragraphs, sections, and clauses rather than fixed token counts. Use NLP tools to detect semantic boundaries.
• Preserve context: Each chunk should contain sufficient contextual information (such as section titles and document names) to remain understandable when removed from the original document.
• Overlap strategy: Maintain 10-15% overlap between adjacent chunks to prevent semantic fragmentation.
• Parent-Child Chunking: Use large paragraphs as "parent chunks" and their sub-paragraphs as "child chunks." During retrieval, search child chunks (for higher precision) but return parent chunks (for contextual completeness). LlamaIndex^[6] natively supports this pattern.
• Multi-granularity indexing: Build multiple granularity indexes for the same document — sentence-level, paragraph-level, document-level — and select the appropriate retrieval granularity based on the nature of the query.

6.4 End-to-End Quality Monitoring

Enterprise-grade Context Engineering systems must have continuous quality monitoring capabilities. We recommend tracking the following five core metrics:

• Retrieval Precision@k: Of the top-k retrieval results, how many are truly relevant? Target: > 85%.
• Answer Faithfulness: How much of the generated answer can be grounded in the retrieved sources? Target: > 90%.
• Context Utilization: Of the information injected into the context, how much was actually used by the model for generation? Low utilization indicates significant noise in the context.
• Latency P95: End-to-end latency from user question to system response (95th percentile). Target: < 3 seconds.
• Hallucination Rate: The proportion of generated responses containing information that cannot be verified in any source. Target: < 5%.

7. Frontier Trends in Context Engineering

Context Engineering is a rapidly evolving field. The following three trends will reshape enterprise AI knowledge architectures over the next 12-18 months.

7.1 Adaptive Context Orchestration

Next-generation Context Engineering systems will no longer rely on predefined rules to determine context composition but will use the LLM itself as a "context controller" — dynamically deciding which knowledge to retrieve, how much conversation history to inject, and which tools to activate based on the characteristics of each query. This meta-cognitive capability allows the system to "think about what information it needs" rather than passively executing a fixed retrieval pipeline.

7.2 Multimodal Context

As multimodal models like Gemini 3^[4] mature, Context Engineering is expanding from pure text to the fusion of images, video, audio, and structured data. Enterprise knowledge bases no longer contain only documents but also engineering drawings, product photos, meeting recordings, and dashboard data. How to build unified indexing and retrieval mechanisms for this multimodal information is the next major technical challenge.

7.3 Personalized Memory Networks

Future AI assistants will maintain a dedicated "memory network" for each user — not only remembering what users have said but also inferring their work patterns, decision preferences, and knowledge blind spots. This personalized memory will transform AI from a "general-purpose tool" into a "personalized intelligent partner." Park et al.'s Generative Agents research^[9] has already demonstrated the initial feasibility of this direction.

8. Conclusion: Context Determines the Upper Bound of Intelligence

In 2026, when LLM capabilities are already sufficiently powerful, a thought-provoking truth emerges: a model's "intelligence" is increasingly less constrained by the model itself and increasingly more determined by the quality of context we provide. This is precisely why Context Engineering is emerging as an independent engineering discipline.

A carefully designed Context Engineering system — combining Agentic RAG's intelligent retrieval, the three-layer memory architecture's knowledge accumulation, multi-agent collaboration capabilities, and rigorous quality monitoring — can elevate the performance of the same foundation model from "barely usable" to "enterprise-grade reliable." This is not incremental improvement but a qualitative transformation.

For enterprises, the ability to build Context Engineering systems will become a core competitive moat in AI. Models can be purchased via APIs, but knowledge architectures, memory systems, and context pipelines must be custom-built around each enterprise's unique knowledge assets. Organizations that complete this construction first will open a significant gap in AI-powered efficiency and decision quality over their competitors.