Context Engineering: Building Reliable AI Systems

The AI industry is undergoing a fundamental shift. While “prompt engineering” dominated conversations in 2023 and 2024, a new discipline has emerged as the critical skill for building production-grade AI systems: context engineering.

If you’ve ever wondered why your AI agent sometimes produces brilliant results and other times fails spectacularly, or why moving from a prototype to production feels impossibly complex, context engineering holds the answers.

In this comprehensive guide, we’ll explore everything you need to know about context engineering in 2025—from foundational concepts to advanced techniques used by leading AI teams to build reliable, scalable, and intelligent AI systems. For a broader view of how AI evolved throughout 2025, see our AI in 2025: Year in Review.

TL;DR — Key Takeaways

Before diving deep, here’s what you need to know:

• Context engineering is the science of curating and managing all information fed to an LLM to maximize its performance across comprehension, reasoning, and real-world application.
• It goes far beyond prompt engineering—managing the entire information ecosystem including system prompts, memory, tools, retrieved documents, and conversation history.
• It’s essential for production AI systems that need to be reliable, accurate, and scalable—particularly for AI agents.
• When implemented correctly, context engineering can reduce hallucinations by up to 90%.
• Andrej Karpathy’s influential analogy: LLM = CPU, Context Window = RAM. Context engineering is about optimally managing that “RAM.”
• Major frameworks like LangChain and LlamaIndex are built specifically to support context engineering workflows.

What is Context Engineering?

The Formal Definition

Context engineering is the art and science of curating, organizing, and maintaining the optimal set of information (tokens) within a Large Language Model’s context window during inference to achieve reliable, accurate, and consistent outputs.

Unlike traditional prompt engineering, which focuses on crafting a single effective prompt, context engineering encompasses the entire information ecosystem that influences how an AI system interprets and responds to requests.

The term gained mainstream recognition in 2025, largely credited to Andrej Karpathy, the renowned AI researcher and former OpenAI co-founder, who described it as:

“The delicate art and science of filling the context window with just the right information for the next step.”

This definition captures a crucial insight: it’s not just about what you ask an LLM, but about everything the LLM knows when you ask it.

The LLM as an Operating System

Karpathy introduced a powerful analogy that has become foundational to understanding context engineering:

Just as operating system engineers obsess over RAM optimization—deciding what to load, when to swap, and how to prioritize—context engineers must optimize the LLM’s “working memory” (the context window).

Every token in the context window is precious real estate. Context engineering is about making every token count.

Core Objectives

Effective context engineering serves several critical objectives:

1. Accuracy: Ensure the AI has access to relevant, current, and accurate information
2. Grounding: Base outputs on verifiable data sources to reduce hallucinations
3. Personalization: Enable tailored responses based on user history and preferences
4. Consistency: Maintain coherent behavior across multiple interactions
5. Efficiency: Optimize token usage for cost and latency management
6. Safety: Enforce guardrails and policy compliance

Components of Context

A complete context window in a production AI system typically includes multiple layers of information:

🔧 System Instructions

• Role definition, behavior guidelines, constraints

📚 Retrieved Knowledge (RAG)

• Documents, data, API responses fetched dynamically

🧠 Memory

• Short-term: Current session state
• Long-term: User preferences, historical patterns

💬 Conversation History

• Previous messages in the current session

🛠️ Tool Definitions

• Available functions, their schemas, and descriptions

🌍 Environmental State

• Current time, user profile, device context

📝 Examples (Few-shot)

• Demonstrations of expected behavior

🛡️ Guardrails

• Safety constraints, policies, prohibited actions

👤 User Input

• The current query or request

Each component must be carefully designed, sized, and positioned to create an optimal context that guides the LLM toward the desired output.

Context Engineering vs. Prompt Engineering

One of the most common questions in 2025 is: “What’s the difference between context engineering and prompt engineering?”

The short answer: context engineering is a superset of prompt engineering. Prompt engineering is one component within the broader discipline of context engineering.

Prompt Engineering: The Foundation

Prompt engineering is the practice of designing, testing, and refining individual inputs (prompts) to guide AI models toward accurate and useful outputs.

It focuses on “what to say to the model at a moment in time” and typically involves:

• Zero-shot prompting: Asking directly without examples
• Few-shot prompting: Providing examples before the query
• Chain-of-thought: Encouraging step-by-step reasoning
• Role-based instructions: Assigning personas to the model

Prompt engineering works excellently for:

• Simple, single-turn interactions
• Classification tasks
• Basic question-answering
• Content generation with clear specifications

Context Engineering: The Complete Picture

Context engineering expands the scope dramatically. It focuses on “what the model knows when you say it—and why it should care.”

It manages the entire information ecosystem:

• System prompts and instructions
• Conversation history across sessions
• Dynamically retrieved documents
• Available tools and their definitions
• User memory and preferences
• Environmental context

Context engineering is essential for:

• Complex, multi-turn conversations
• Autonomous AI agents
• Production systems requiring reliability
• Enterprise applications with compliance needs

Comparison Table

Why the Shift Matters

As AI models become more sophisticated, the barrier to getting decent responses from a single prompt has lowered significantly. Models like GPT-4, Claude 3.5, and Gemini 1.5 are remarkably capable at understanding user intent.

However, building production AI systems that are:

• Reliable across thousands of user interactions
• Consistent in tone and accuracy
• Compliant with enterprise policies
• Cost-effective at scale
• Secure against prompt injection

…requires context engineering.

The prompt is just one piece. The context is everything.

Understanding the Context Window

What is a Context Window?

The context window is the maximum amount of text (measured in tokens) that an LLM can process at once. Think of it as the model’s “short-term memory”—everything the model can “see” and consider when generating a response.

A token is typically a subword or part of a word. For a deeper understanding of tokenization and context windows, see the guide on tokens, context windows, and parameters. As a rough approximation:

• 1 token ≈ 4 characters in English
• 100 tokens ≈ 75 words
• 1,000 tokens ≈ 750 words

Context Window Sizes in 2025

Models have dramatically expanded their context windows:

For an overview of these models and their capabilities, see the guide on understanding the AI landscape. These massive context windows enable new possibilities—but they also introduce new challenges.

The “Lost in the Middle” Problem

Research has consistently shown that LLMs don’t treat all parts of the context window equally. They tend to:

• Remember well: Information at the beginning of the context
• Remember well: Information at the end of the context
• Struggle with: Information in the middle of the context

This phenomenon, called the “Lost in the Middle” problem, has significant implications for context engineering strategies. You must account for this by:

• Placing critical information at the beginning and end
• Summarizing middle sections for key points
• Using retrieval to inject relevant info close to the query

Why Bigger Isn’t Always Better

It might seem like larger context windows solve all problems—just load everything in! But this approach has serious drawbacks:

1. Cost: LLM APIs charge per token. More context = higher costs.
2. Latency: Processing more tokens takes longer. 2M tokens will be slower than 2K tokens.
3. Information Overload: Models can get “confused” by excessive, irrelevant information.
4. Lost in the Middle: More content means more middle sections where information gets overlooked.

Optimal Context Window Utilization

Research suggests that 40-70% utilization of the context window often produces the best results. This means:

• Don’t fill it to capacity just because you can
• Every token should serve a clear purpose
• Quality of context matters more than quantity
• “Relevance first” should be your guiding principle

Context engineering is about using the context window wisely, not just fully.

Core Techniques and Strategies

Now let’s explore the essential techniques that form the toolkit of context engineering.

1. Retrieval-Augmented Generation (RAG)

RAG is the foundational pattern of context engineering. It combines information retrieval with language generation to ground AI responses in external knowledge. For a comprehensive deep dive, see the guide on RAG, Embeddings, and Vector Databases.

How RAG Works

1. User Query: “What are the refund policies for enterprise plans?”
2. Embedding & Retrieval: Convert query to vector, search vector database for similar documents, retrieve top-k relevant chunks
3. Context Assembly: Combine system prompt + retrieved docs + query, apply formatting and token budgeting
4. LLM Generation: Generate response grounded in retrieved information
5. Response: “Enterprise plans include a 30-day money-back guarantee with prorated refunds after…”

Benefits of RAG

• Reduces hallucinations: Responses grounded in real documents
• Enables current information: Access data beyond training cutoff
• Supports proprietary data: Use internal company knowledge
• Provides citations: Trace answers back to sources
• Cost-effective: Retrieve only what’s needed

2. Memory Management Strategies

For conversational AI and agents, memory management is critical for maintaining coherence across interactions.

Short-Term Memory (Session State)

Short-term memory maintains context within a single conversation session:

• Full history: Keep all messages (works for short conversations)
• Sliding window: Keep only the last N messages
• Importance weighting: Prioritize messages marked as important

# Example: Sliding window memory
def manage_short_term_memory(messages, max_messages=20):
    if len(messages) > max_messages:
        # Keep system prompt + recent messages
        system = messages[0]
        recent = messages[-(max_messages-1):]
        return [system] + recent
    return messages

Long-Term Memory (Persistent Storage)

Long-term memory persists information across sessions:

• User preferences: Stored settings and choices
• Entity extraction: Key facts about users/topics
• Interaction patterns: Learning from past behavior

Memory Techniques

Compaction: When approaching context limits, summarize the conversation and restart with the summary.

Structured Note-Taking: Agents maintain a “scratchpad” with key findings:

## Agent Scratchpad
- User goal: Find flights to Tokyo under $800
- Preferred dates: March 15-22, 2025
- Must have: Window seat, vegetarian meal
- Already checked: Delta (too expensive), United (no availability)
- Next step: Check Japan Airlines and ANA

Entity and Preference Extraction: Store structured data about users:

{
  "user_id": "u_12345",
  "preferences": {
    "communication_style": "concise",
    "expertise_level": "advanced",
    "timezone": "America/New_York"
  },
  "entities": {
    "company": "Acme Corp",
    "role": "Engineering Manager"
  }
}

3. Context Compression and Pruning

Not everything needs to be in the context window. Compression techniques help include more information in less space.

Summarization

Use a smaller, faster LLM to summarize documents before injection:

• Original document: 5,000 tokens
• Summary: 500 tokens (10% of original)

Importance-Based Filtering

Score chunks by relevance and include only the top results.

Metadata Filtering

Filter documents before retrieval based on metadata like date, category, or language.

4. Document Chunking Strategies

How you split documents significantly impacts retrieval quality.

Optimal Chunk Sizes

Research suggests 512-1024 tokens as the sweet spot for most use cases:

• Too small: Loss of context, incomplete information
• Too large: Diluted relevance, wasted tokens
• Just right: Complete thoughts with focused relevance

Chunking Techniques

Fixed-size with overlap: Overlapping chunks (e.g., 300 tokens with 50-token overlap)

Semantic chunking: Split at natural boundaries like paragraph breaks, section headers, or sentence boundaries

Hierarchical chunking: Different granularities for different purposes—document-level for broad context, section-level for topic focus, paragraph-level for specific details

5. Re-ranking and Selective Inclusion

Initial retrieval often includes marginally relevant results. Re-ranking improves precision.

Popular re-ranking approaches:

• Cross-encoder models: More accurate but slower
• LLM-based re-ranking: Use the LLM to score relevance
• Reciprocal Rank Fusion: Combine multiple retrieval methods

6. Tool Design for Context Efficiency

When AI agents use tools, those tool definitions consume context tokens.

Best Practices for Tool Design

Be concise but complete:

{
  "name": "search_products",
  "description": "Search product catalog by name, category, or price range",
  "parameters": {
    "query": "Search terms",
    "category": "Optional: filter by category",
    "max_price": "Optional: maximum price in USD"
  }
}

Minimize overlap: Don’t create multiple tools that do similar things.

Dynamic tool loading: Only include tools relevant to the current task.

7. Multi-Agent Architectures (Context Isolation)

For complex tasks, multiple specialized agents can work together while maintaining isolated contexts.

The orchestrator agent maintains the main context, while specialized sub-agents (Research Agent, Writer Agent, Editor Agent) each operate with isolated contexts. Each sub-agent:

• Has its own optimized context
• Focuses on a specific task
• Returns a condensed summary
• Prevents “context pollution” in the main agent

Building Context-Aware AI Agents

AI agents represent the cutting edge of LLM applications—systems that can reason, plan, and take actions autonomously. Context engineering is absolutely critical for agent success.

Why Context Engineering is Critical for Agents

Agents perform complex, multi-step tasks that require:

• Coherence: Remembering what was done in previous steps
• Continuity: Maintaining goals across many interactions
• Goal-directed behavior: Staying focused on the objective

Without proper context management, agents:

• Lose track of their progress
• Repeat actions unnecessarily
• Get confused by irrelevant information
• Generate inconsistent outputs
• Incur unnecessary costs from bloated contexts

Andrej Karpathy’s Vision: “The Decade of Agents”

In 2025, Karpathy offered a sobering but realistic perspective:

“2025 is not ‘the year of agents.’ It’s the beginning of ‘the decade of agents.’”

He identifies several challenges still facing autonomous agents:

• Reliable memory: Agents struggle with long-horizon tasks
• Multimodal understanding: Integrating vision, audio, and text
• Continuous learning: Adapting to new information
• Recovery from errors: Graceful failure handling
• Human oversight: The need for “partial autonomy”

Karpathy advocates for generation-verification loops—agents that generate, verify, and iterate rather than attempting fully autonomous operation.

Agent Context Components

A well-designed agent context includes:

🎯 Task Instructions: What the agent should accomplish

🛠️ Available Tools: What actions the agent can take (e.g., web_search, read_page, save_note)

📝 Current State / Scratchpad: Progress tracking and notes

📚 Retrieved Information: Dynamically loaded based on current step

🛡️ Guardrails: What the agent should NOT do

Best Practices for Agent Context Design

1. Break complex tasks into sub-tasks: Each step should have focused context.

2. Maintain a working scratchpad: Agents should write notes as they work.

3. Just-in-time retrieval: Fetch information when needed, not upfront.

4. Clear system prompt calibration: Specific enough to guide, flexible enough to adapt.

5. Robust error recovery: Define what to do when things go wrong.

6. Explicit state management: Always know where the agent is in the workflow.

Tools and Frameworks

The ecosystem for context engineering has matured significantly in 2025. Here are the leading tools and frameworks.

LangChain

LangChain is the most comprehensive framework for building LLM applications. Learn how to use it in our guide to building AI applications.

Strengths:

• Complex reasoning and multi-step workflows
• Extensive integrations (100+ tools, retrievers, LLMs)
• Agent orchestration with LangGraph
• Observability with LangSmith

Best for: Versatile agent development, rapid prototyping, complex chains

from langchain.agents import create_react_agent
from langchain.tools import DuckDuckGoSearchTool
from langchain_openai import ChatOpenAI

llm = ChatOpenAI(model="gpt-4o")
tools = [DuckDuckGoSearchTool()]
agent = create_react_agent(llm, tools)

LlamaIndex

LlamaIndex is the leading data-centric framework for RAG applications.

Strengths:

• Advanced indexing and data ingestion
• Optimized retrieval and query engines
• Chunk optimization and hybrid search
• Excellent for document-heavy applications

Best for: Knowledge-intensive applications, document processing, enterprise search

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader

documents = SimpleDirectoryReader("./data").load_data()
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine()
response = query_engine.query("What are the key features?")

Hybrid Approach: LangChain + LlamaIndex

The most sophisticated applications in 2025 combine both:

• LlamaIndex for data ingestion, indexing, and retrieval
• LangChain for agent orchestration, reasoning, and tool use

Other Notable Tools

Best Practices for Production

Moving from prototype to production requires rigorous context engineering. Here are the essential best practices.

The 7 Principles of Context Engineering

1. Relevance First: Every token must serve a clear purpose. Ask: “Does this information help with the current task?”

2. Provenance and Trust: Make all injected information traceable to verified sources. Know where every piece of context comes from.

3. Compression Over Completeness: Prefer concise representations over exhaustive ones. A good summary beats a full document.

4. Version Control: Track prompts, templates, and retrieval pipelines like code. Use Git, maintain changelogs.

5. Clear Ownership: Assign responsibility for each context component. Who maintains the system prompt? Who updates the knowledge base?

6. Automated Policy Enforcement: Implement guardrails programmatically—data sensitivity checks, prompt injection prevention, content filtering.

7. Continuous Optimization: Measure, iterate, improve. Context engineering is never “done.”

Token Budgeting

Establish explicit budgets for each context component:

Total Context Budget: 8,000 tokens
├── System Prompt:         500 tokens (6%)
├── User Profile:          200 tokens (2.5%)
├── Retrieved Documents: 3,000 tokens (37.5%)
├── Conversation History:1,500 tokens (19%)
├── Tool Definitions:      500 tokens (6%)
├── Current Query:         300 tokens (4%)
└── Reserved for Output: 2,000 tokens (25%)

Structured Formatting

Use consistent, parseable formats:

## System Instructions
You are a helpful customer service agent for TechCorp.

## Customer Context
- Name: Jane Smith
- Account Type: Enterprise
- Open Tickets: 2

## Retrieved Knowledge
<document source="refund_policy.md">
Enterprise customers are eligible for full refunds within 60 days...
</document>

## Current Query
"Can I get a refund for my purchase last month?"

Security Considerations

Prompt injection prevention:

• Validate and sanitize all user inputs
• Use delimiters to separate user content from instructions
• Monitor for injection patterns

Data sensitivity:

• Classify data before including in context
• Redact PII when not necessary
• Implement access controls for knowledge bases

Audit trails:

• Log what context was used for each request
• Enable tracing for debugging and compliance
• Maintain records for regulated industries

Enterprise Use Cases

Context engineering powers critical AI applications across industries.

Customer Service and Support

Challenge: Agents need access to customer history, product docs, and policies while maintaining consistent tone.

Solution Architecture: User Query → Intent Detection → Context Assembly (Customer profile from CRM, Recent support tickets, Relevant product documentation, Company policies and procedures) → LLM → Response

Impact: 40% reduction in escalations, 60% faster resolution times.

AI Coding Assistants

Challenge: Understanding project architecture, dependencies, and coding patterns across large codebases.

Context Engineering Approach:

• Index entire codebase with semantic chunking
• Track file dependencies and relationships
• Maintain session context of recent edits
• Include relevant documentation and style guides

Examples: GitHub Copilot, Cursor, Cody

Financial Services

Challenge: Highly regulated environment requiring compliance, accuracy, and audit trails.

Use Cases:

• Regulatory FAQ bots with compliance-approved responses
• Credit policy copilots for loan officers
• Risk analysis assistants with real-time data
• Private banking advisors with client investment profiles

Context Requirements:

• Versioned, approved knowledge bases
• Strict guardrails on recommendations
• Complete audit logging
• Real-time market data integration

Healthcare

Challenge: Integrating patient data while ensuring HIPAA compliance and clinical accuracy.

Context Engineering Considerations:

• Separate PHI from general medical knowledge
• Include relevant clinical guidelines
• Maintain strict access controls
• Never use AI for diagnosis—only documentation assistance

Common Challenges and Solutions

Context Drift and Rot

Problem: Information becomes stale or conversations drift from the original goal. This is one of the key reasons why AI agents get dumber over time.

Solutions:

• Regular context refresh cycles
• Freshness signals in document metadata
• Periodic goal re-anchoring in long conversations
• Version tracking for knowledge bases

Information Overload

Problem: Too much context degrades performance rather than improving it.

Solutions:

• Aggressive relevance filtering
• Importance scoring with thresholds
• Just-in-time retrieval instead of pre-loading
• Maximum token budgets per component

Siloed Data Sources

Problem: Enterprise data scattered across many systems.

Solutions:

• Unified data layer / knowledge graph
• API integrations with major systems
• Regular synchronization pipelines
• Centralized embedding and indexing

Cost Optimization

Problem: Token costs scale with context size.

Solutions:

• Context compression and summarization
• Caching frequently-used contexts
• Smaller models for preprocessing tasks
• Tiered approach: small model first, large model when needed

Latency Management

Problem: Large contexts = slower responses.

Solutions:

• Parallel retrieval operations
• Precomputed context for common scenarios
• Streaming responses for perceived speed
• Edge caching for static components

The Future of Context Engineering

From Infrastructure to Design

Model providers are building context management capabilities directly into LLMs:

• Native Chain of Thought reasoning
• Built-in memory and retrieval
• Context editing and management tools

This means context engineering is transitioning from low-level infrastructure work to high-level design decisions.

Context as Cognitive Architecture

A key insight for the future:

The context system will define AI capability more than the choice of the underlying model.

Memory architecture, context lifecycle management, and information orchestration are becoming the primary differentiators between AI applications.

Self-Optimizing Systems

We’re seeing early signs of AI systems that optimize their own context:

• Learning which documents are most useful
• Automatically adjusting token budgets
• Evolving retrieval strategies based on feedback

Predictions for 2026 and Beyond

1. Standardization: Common patterns and best practices will solidify
2. Dedicated roles: “Context Engineer” becomes a recognized job title
3. Platform integration: Context engineering tools built into MLOps/LLMOps
4. Automated optimization: AI-assisted context tuning becomes standard

Getting Started

Your First Steps

1. Start with basic RAG: Don’t over-engineer initially. Get retrieval working, then iterate.

2. Choose the right tools: Pick frameworks with good documentation and active communities. LangChain + LlamaIndex is a solid starting point.

3. Implement version control: Treat prompts and configurations as code from day one.

4. Measure everything: Set up logging and evaluation pipelines early. You can’t improve what you don’t measure.

5. Iterate based on real data: Launch with users, collect feedback, and continuously refine your context strategies.

Resources for Learning

• LangChain Documentation: Comprehensive tutorials and examples
• LlamaIndex Documentation: Deep dives into RAG patterns
• Anthropic’s Claude Documentation: Best practices for context design
• Andrej Karpathy’s talks: Search for his presentations on context and agents

FAQs

What is context engineering in AI?

Context engineering is the discipline of designing and managing all information provided to a Large Language Model (LLM) to maximize its performance. This includes system prompts, conversation history, retrieved documents, tool definitions, memory, and environmental context—everything the model “knows” when generating a response.

How is context engineering different from prompt engineering?

Prompt engineering focuses on crafting individual prompts for single interactions. Context engineering is broader—it manages the entire information ecosystem surrounding an AI system, including memory across sessions, dynamic data retrieval, tool orchestration, and multi-turn conversation management. Prompt engineering is a subset of context engineering.

Why is context engineering important in 2025?

As AI systems become more complex and agentic, simply crafting good prompts isn’t enough. Production AI applications need to be reliable, consistent, cost-effective, and secure across thousands of interactions. Context engineering provides the framework for building AI systems that meet these production requirements.

What are the best tools for context engineering?

The leading frameworks are:

• LangChain: Best for agent orchestration and complex workflows
• LlamaIndex: Best for RAG and data-intensive applications
• LangSmith: Best for observability and evaluation
• Haystack: Best for production search pipelines

Many teams combine LangChain + LlamaIndex for comprehensive capabilities.

How can context engineering reduce AI hallucinations?

By grounding LLM responses in retrieved, verified information (through RAG) and providing precise, relevant context, context engineering dramatically reduces the likelihood of hallucinations. Studies show up to 90% reduction in hallucination rates when proper context engineering is implemented.

What is the “lost in the middle” problem?

LLMs tend to recall information from the beginning and end of their context window much better than information in the middle. This means strategic placement of important information matters. Context engineering strategies address this by prioritizing information placement and using summarization for middle sections.

How much of the context window should I use?

Research suggests 40-70% utilization is often optimal. Using 100% of the context window can lead to information overload, increased costs, and degraded performance. Focus on relevance over quantity—every token should serve a purpose.

What’s the relationship between RAG and context engineering?

RAG (Retrieval-Augmented Generation) is a foundational technique within context engineering. It’s the pattern of retrieving relevant documents and injecting them into the LLM’s context. Context engineering encompasses RAG plus memory management, tool orchestration, conversation handling, and more.

Conclusion

Context engineering has emerged as the critical discipline for building production-grade AI systems in 2025. While prompt engineering taught us to communicate effectively with LLMs, context engineering teaches us to architect the entire information environment that enables AI to perform reliably at scale.

The key insights to remember:

• Context is everything: The prompt is just one piece; the entire context determines AI behavior.
• Quality over quantity: A well-curated 2,000 token context often outperforms a bloated 100,000 token one.
• Systems thinking: Design the architecture—retrieval, memory, tools, guardrails—not just individual prompts.
• Measure and iterate: Context engineering is empirical; test and refine continuously.

As Andrej Karpathy reminds us, we’re at the beginning of the “decade of agents.” The teams that master context engineering will build the AI systems that define this era.

Start small, measure everything, and never stop optimizing. The context window is your canvas—engineer it wisely.

Related Articles

• AI Agents vs Agentic AI: The Definitive Guide — Understand the agent architecture that relies on context engineering
• Why Your AI Agent Gets Dumber Over Time — Practical examples of context engineering failures and fixes
• Agentic Browsers: The Complete Guide — How context powers AI-driven web browsing
• RAG, Embeddings, and Vector Databases — Deep dive into the foundational RAG pattern
• Tokens, Context Windows & Parameters — Understanding the technical fundamentals
• Building Your First AI-Powered Application — Hands-on guide using LangChain
• What Are Large Language Models? — The technology that context engineering optimizes

Last updated: December 2025

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