Best Practices In Context Engineering

The problem with AI is that it's fantastic at tightly-scoped tasks, like winning at chess or go OR even at something like folding proteins, but it struggles immensely in defining the context and appropriate scope of the task -- this should come as no surprise, the biggest problem in managing or leading software engineering teams is also scope management or requirements management and not simply adding another neato feature because it seems cool.

DISCIPLINE and telling AI to knock it the fuck off is where context engineering comes in. Context engineering is the practice of designing and managing the information provided to large language models (LLMs) to ensure that they don't squander their CPU/memory resources, so that they can effectively, expeditiously FINISH performing tasks. Drawing boundaries or providing TIGHT CONTEXT involves curating, structuring, annotating information and notifying relevant parties to be aware of developments in an area of knowledge relevant to them to optimize LLM performance, going beyond simple goofbutt prompt crafting.

Claude's Take On Advanced Frameworks and Reproducible Implementations

Context engineering for Large Language Models in scientific and engineering research has evolved from basic prompt optimization to sophisticated multi-modal, multi-agent systems capable of autonomous research workflows. Recent theoretical advances enable context windows exceeding 2 million tokens, while specialized frameworks like PaperQA2 demonstrate superhuman performance on scientific tasks (86.3% vs. 57.9% for GPT-4 on PubMedQA). This comprehensive analysis synthesizes cutting-edge research, proven implementations, and optimization methodologies specifically tailored for advanced practitioners in scientific domains. The field is poised for transformative growth through foundation models designed for science, neurosymbolic reasoning architectures, and automated context engineering systems.

Current state of foundational research reveals unprecedented scaling capabilities

The theoretical landscape of context engineering has undergone revolutionary changes in 2023-2025, with mathematical frameworks enabling context windows beyond 2 million tokens while maintaining near-lossless performance. LongRoPE demonstrates context scaling to 2M+ tokens with minimal degradation, while implementations provide practical scaling recipes. These advances stem from sophisticated attention mechanisms including Dual Chunk Attention (DCA), which decomposes attention computation for long sequences, and Infini-Attention, offering compressive memory for theoretically infinite contexts.

Mathematical foundations now provide rigorous frameworks for context optimization. Information-theoretic approaches quantify context efficiency through entropy calculations and intrinsic dimension measurements. The axiomatic decomposition of LLM reasoning effects provides mathematical guarantees for separating memorization from in-context reasoning, enabling faithful analysis of confidence scores. These theoretical advances directly address the fundamental challenge of context utilization optimization in scientific applications.

Training efficiency research demonstrates that 500M-5B tokens sufficiently extend context to 128K tokens, with domain balance and length upsampling strategies proving crucial for maintaining performance across content types. Progressive extension methodologies show that non-uniform positional interpolation significantly outperforms linear scaling approaches, providing concrete guidance for implementing long-context systems in research environments.

Scientific applications demonstrate mature context engineering patterns

Real-world implementations reveal sophisticated context engineering patterns specifically optimized for scientific research workflows. PaperQA2 achieves superhuman performance through document metadata-awareness, LLM-based re-ranking, and contextual summarization, outperforming GPT-4 by 30 points on scientific literature tasks. The system integrates citation data, journal quality metrics, and full-text search into coherent context representations that preserve scientific rigor.

Mathematical reasoning systems showcase advanced context architectures through hierarchical proof decomposition. DeepSeek-Prover's 52% accuracy on miniF2F (surpassing GPT-4's 23%) demonstrates the effectiveness of iterative synthetic data generation combined with formal verification. LeanDojo provides open-source infrastructure for theorem proving with fine-grained premise annotations, while ProD-RL introduces reinforcement learning for tree-structured proof decomposition that rewards partial progress.

Multi-modal scientific reasoning has achieved breakthrough performance through sophisticated context fusion strategies. MathVerse benchmarks reveal that most models struggle with visual mathematical reasoning, yet successful implementations like GPT-4V demonstrate effective integration of diagrams, equations, and textual descriptions. Chemistry-specific models like MoLFormer handle SMILES strings through linear attention with rotary embeddings, processing over 1 billion molecules from ZINC and PubChem databases.

Scientific computing applications leverage context engineering for autonomous simulation workflows. Autonomous Simulation Agent (ASA) demonstrates near-flawless execution of experimental design, execution, analysis, and reporting cycles for polymer chain conformations. The bilevel optimization approach combines outer-level LLM reasoning with inner-level simulation optimization, showing particular effectiveness in constitutive law search and molecular design applications.

Technical implementations provide immediately deployable frameworks

Production-ready frameworks offer distinct advantages for different research contexts. LangChain excels in modular context-aware application design with comprehensive tool integrations, while LlamaIndex optimizes document-first workflows with advanced indexing and metadata extraction capabilities specifically designed for scientific literature. Haystack provides enterprise-grade production features with monitoring and evaluation tools, making it suitable for large-scale research institutions.

Context compression achieves remarkable efficiency gains through multiple complementary approaches. LLMLingua delivers up to 20x compression ratios while maintaining 95%+ accuracy, with LLMLingua-2 providing 3x-6x speed improvements. Selective Context demonstrates 40% memory savings with 2x content processing capacity, while In-Context Autoencoder (ICAE) shows 4x compression with improved inference latency through learnable encoder architectures.

Memory system architectures enable persistent research sessions through sophisticated state management. Mem0 demonstrates 26% higher accuracy than OpenAI Memory with 91% lower p95 latency, achieving 90% token savings through intelligent memory optimization. MemGPT/Letta frameworks implement two-tier memory systems combining context windows with external storage, while A-MEM provides Zettelkasten-inspired organization with dynamic link generation for multi-hop reasoning support.

Multi-agent coordination patterns have matured into production-ready frameworks for complex research workflows. LangGraph's graph-based architecture supports sophisticated orchestration with cycles and conditionals, enabling human-in-the-loop research processes with built-in memory and streaming capabilities. AutoGen focuses on conversational multi-agent systems with secure code execution environments, while CrewAI provides role-based team structures with hierarchical task delegation specifically designed for research applications.

Performance optimization enables systematic efficiency improvements

Comprehensive evaluation methodologies provide rigorous frameworks for measuring context engineering effectiveness. HELM's seven-dimensional approach (accuracy, calibration, robustness, fairness, bias, toxicity, efficiency) covers 42 scenarios with 96% model coverage, establishing standardized benchmarks for scientific applications. BigBench's 214+ tasks include domain-specific evaluations through SciBench, MathVista, and MultiMedQA, while specialized tools like LongCodeBench address real-world software engineering contexts.

Token efficiency optimization achieves substantial cost reductions through systematic approaches. Prompt optimization delivers up to 50% token reduction while maintaining accuracy, with model selection strategies showing 100x cost differences between appropriate model tiers (GPT-4o Mini vs. GPT-4 Turbo). Batching and caching implementations demonstrate near-perfect linear scaling with doubled batch sizes, while response caching reduces input costs by 50% for repeated queries.

Context degradation monitoring provides quantitative frameworks for maintaining quality across extended conversations. Optimal performance occurs at 75-85% of maximum context length, with quality decline typically beginning after 90% context utilization. Hierarchical summarization maintains 95%+ critical information preservation, while sliding window approaches with selective retention prevent performance degradation in long research sessions.

Scalability patterns demonstrate efficient resource utilization across different deployment scenarios. Linear scaling achieves effectiveness up to 256 GPUs with 70-80% GPU utilization targets for optimal cost-performance ratios. Model parallelism strategies including tensor, pipeline, and data parallelism enable processing of models requiring hundreds of gigabytes of memory, with hybrid approaches optimizing resource allocation for specific research workloads.

Emerging directions shape the next generation of scientific AI

Foundation models specifically designed for scientific applications represent the most significant emerging trend. Meta's Galactica failure demonstrated that scientific accuracy requires more sophisticated context engineering than general-purpose scaling, leading to purpose-built architectures that embed domain knowledge, verification mechanisms, and uncertainty quantification directly into model designs. Microsoft Research AI for Science and the SciFM25 initiative highlight national-scale efforts developing foundation models for materials science, climate research, and life sciences.

Multimodal scientific reasoning advances through sophisticated context fusion architectures that seamlessly integrate text, equations, molecular structures, and experimental data. Multimodal Knowledge Graph (MR-MKG) approaches enhance reasoning through cross-modal alignment, while emerging frameworks handle multi-resolution visual contexts from molecular to macroscopic scales. These systems enable transfer learning across scientific domains through abstract pattern recognition and universal scientific visual vocabularies.

Neurosymbolic approaches demonstrate breakthrough potential through architectures combining neural creativity with symbolic verification. AlphaGeometry-style systems expand beyond mathematics to chemistry, biology, and physics, with 167 papers published 2020-2024 showing rapid growth in learning, inference, logic, reasoning, and knowledge representation applications. Future implementations include scientific hypothesis machines, laboratory planning systems, and interdisciplinary discovery engines that automate cross-domain pattern recognition.

Automated context engineering eliminates the need for specialized prompt engineering through meta-learning approaches. Auto-ICL frameworks automatically generate contextual examples that outperform manual prompt engineering, while many-shot in-context learning scaling and parallel context processing (ParaICL) enable dynamic adaptation based on problem complexity and domain requirements. Self-improving context libraries through scientific feedback create systems that continuously optimize their own context representations.

Implementation roadmap for advanced practitioners

Individual researchers should begin with PaperQA2 + LlamaIndex for literature analysis, Mem0 for session persistence, and LLMLingua for cost optimization, with monthly costs typically ranging $50-100. This configuration provides immediate access to superhuman scientific literature capabilities with minimal setup complexity.

Small research teams (2-5 people) benefit from LangGraph + CrewAI for multi-agent coordination, PaperQA2 + Haystack for production RAG, and Letta + Mem0 for collaborative memory systems. LLMLingua-2 provides enhanced compression for team workflows, with costs typically $200-500/month for comprehensive research automation.

Large research institutions require custom LangGraph + AutoGen implementations with Haystack + custom RAG solutions for specialized requirements. Self-hosted models reduce API dependencies while custom memory systems provide institutional knowledge persistence. Investment exceeds $1000/month but enables completely autonomous research capabilities with institutional-scale knowledge integration.

The technical stack evolution follows predictable patterns: 2025 will see deployment of specialized scientific foundation models in narrow domains with multimodal context engineering in laboratory systems. 2026 brings cross-domain reasoning systems with automated context engineering reducing prompt engineering requirements. 2027 delivers fully integrated scientific discovery ecosystems combining simulation, experimentation, and AI reasoning with autonomous hypothesis generation and validation cycles.

Conclusion

Context engineering for scientific research has evolved beyond prompt optimization to encompass sophisticated theoretical frameworks, production-ready implementations, and systematic optimization methodologies. The convergence of million-token context windows, superhuman scientific reasoning capabilities, and automated context generation creates unprecedented opportunities for AI-enhanced research workflows. Success requires understanding both theoretical foundations and practical implementations, with emphasis on reproducible research practices and systematic evaluation methodologies. The field's rapid evolution demands continuous adaptation, but the frameworks and patterns identified here provide stable foundations for immediate implementation and future extension.

Advanced practitioners can leverage these insights to implement context engineering systems that exceed human performance on scientific tasks while maintaining the rigor and reproducibility essential for research applications. The combination of proven frameworks, optimization techniques, and emerging technologies creates a clear pathway for transforming scientific research through sophisticated AI integration.

Grok's Take On Context Engineering Best Practices

Key Points:

  • Context engineering involves curating and structuring information to optimize large language model (LLM) performance, going beyond simple prompt crafting.
  • It seems likely that effective context engineering enhances LLM reliability by providing relevant data, tools, and formats, though challenges like token costs and context degradation persist.
  • Research suggests practices like structured state management, compression, and multi-agent systems improve outcomes, but there’s ongoing debate about balancing technical and organizational needs.
  • The term "context engineering" is gaining traction over "prompt engineering," reflecting its broader scope, though some argue it overlaps with intent or communication engineering.

What is Context Engineering? Context engineering is the practice of designing and managing the information provided to LLMs to ensure they can effectively perform tasks. Unlike prompt engineering, which focuses on crafting specific instructions, context engineering involves dynamically assembling relevant data, tools, and formats to create a comprehensive context window. This process is critical for complex AI applications, such as agentic systems, where LLMs need to handle long-running tasks or integrate with external tools.

Why It Matters As LLMs become more powerful, their ability to process and utilize context determines their effectiveness. Poorly managed context can lead to irrelevant or incorrect outputs, while well-engineered context can transform an LLM into a highly capable assistant. This is particularly important in industrial applications where tasks require specific, often organizationally unique, information.

Core Practices

  • Track Token Usage: Monitor and manage token consumption to optimize costs and performance.
  • Structure Context: Use schemas to organize data, ensuring only relevant information is included.
  • Compress Data: Summarize outputs from tools to keep context manageable.
  • Implement Memory: Use simple memory systems to track and update agent states.
  • Leverage Multi-Agent Systems: Employ multiple agents for parallel tasks to enhance efficiency.
  • Dynamic Context Assembly: Build systems that adaptively fetch and format context from various sources.

Relevant Research Recent studies, such as those on arXiv, explore scaling context lengths and attributing model outputs to specific context parts, offering tools to refine context engineering practices. These findings suggest that with proper data engineering, LLMs can handle extended contexts, improving their utility in real-world applications.

Comprehensive Overview of Context Engineering Best Practices

Context engineering has emerged as a pivotal discipline in the development of AI applications, particularly those leveraging large language models (LLMs). It encompasses the strategic curation, structuring, and management of information provided to LLMs to optimize their performance across diverse tasks. This section provides a detailed exploration of best practices, drawing from recent discussions, research papers, and industry insights, including those referenced in the provided document by Lance Martin and related X posts.

Defining Context Engineering

Context engineering is the art and science of building dynamic systems that supply LLMs with the right information and tools in an appropriate format to accomplish tasks effectively. As articulated by Andrej Karpathy in an X post on June 25, 2025 (Karpathy's Post), it involves filling the LLM’s context window with task descriptions, few-shot examples, retrieval-augmented generation (RAG) data, multimodal inputs, tools, state, and history. This process requires balancing the amount and relevance of information to avoid performance degradation or excessive costs, blending scientific precision with an intuitive understanding of how LLMs process context.

The LangChain blog post on June 23, 2025 (The Rise of Context Engineering), further defines context engineering as distinct from prompt engineering, emphasizing its focus on assembling dynamic data rather than just crafting instructions. It highlights that context engineering is becoming the most critical skill for AI engineers as applications evolve from single prompts to complex, agentic systems.

Best Practices in Context Engineering

The following best practices are synthesized from the document by Lance Martin (Context Engineering), X posts, and related resources, providing actionable strategies for effective context engineering.

  1. Instrumentation for Token Management

    • Description: Tracking token usage is essential to identify and mitigate excessive consumption, particularly from tool calls, which can inflate costs and degrade performance.
    • Implementation: Use monitoring tools to track token usage and isolate token-heavy operations. This sets the stage for optimizing context engineering efforts.
    • Reference: Hamel’s blog on evaluations (Evals Blog) emphasizes the importance of instrumentation in managing LLM performance.

  2. Structured State Management

    • Description: Define a well-structured state schema to control the information exposed to the LLM, avoiding bloated message lists that can overwhelm the model.
    • Implementation: Use frameworks like Pydantic to create structured schemas. For example, Anthropic’s research system saves research plans for future use, ensuring consistent context (Anthropic’s Multi-Agent System).
    • Example: A state schema might include fields for task objectives, user preferences, and recent interactions, ensuring only relevant data is included.

  3. Compression at Tool Boundaries

    • Description: Summarize outputs from token-heavy tool calls to prevent context growth, maintaining efficiency.
    • Implementation: Use a smaller LLM with straightforward prompting to compress tool outputs. For instance, Claude Code auto-compacts context at 95% of the context window (Claude Code Best Practices).
    • Benefit: Reduces token costs and maintains model focus on relevant information.

  4. Simple Memory Systems

    • Description: Implement basic memory systems to track and update agent states, enhancing context continuity.
    • Implementation: Use file-based memory to store preferences or states, updated via LLM based on human feedback. An example is an email assistant tracking user preferences (LangChain Agents).
    • Tools: Frameworks like Letta, Mem0, LangGraph, Zep, or Neo4J can facilitate memory management.

  5. Multi-Agent Systems for Parallel Tasks

    • Description: Employ multiple agents for tasks that can be parallelized, improving efficiency but requiring careful coordination.
    • Implementation: Anthropic’s multi-agent researcher outperformed single-agent systems by 90.2%, though it used 15× more tokens (Anthropic’s Multi-Agent System).
    • Challenge: Coordination in multi-agent systems can be complex, requiring robust context isolation strategies.

  6. Dynamic Context Assembly

    • Description: Build systems that dynamically fetch and format context from multiple sources, such as user inputs, previous interactions, Juno, tool outputs, and external data.
    • Implementation: Use retrieval systems to dynamically insert relevant information into prompts, as discussed in the LangChain blog (The Rise of Context Engineering).
    • Example: An agent might fetch user preferences from a database and combine them with real-time tool outputs.

  7. Format and Structure Optimization

    • Description: The format of context significantly impacts LLM performance. Clear, consistent formatting enhances pattern recognition.
    • Implementation: Ensure consistent formatting of examples and instructions, as noted in a Reddit post on in-context learning (Reddit Post). For example, order examples from simple to complex or place the most relevant ones last.
    • Insight: @omarsar0 on X emphasizes structuring inputs/outputs, including schema definitions, to improve clarity (Omar's Post).

  8. Evaluation and Iteration

    • Description: Continuously evaluate whether the context enables the LLM to plausibly accomplish the task, iterating as needed.
    • Implementation: Use observability tools like LangSmith to trace agent calls and identify context-related issues (LangSmith).
    • Question: As per the LangChain blog, ask, “Can it plausibly accomplish the task?” to diagnose context deficiencies.

Challenges in Context Engineering

  • Context Degradation Syndrome: Overloading the context window with irrelevant data can reduce performance, as noted by Karpathy (Karpathy's Post).
  • Token Costs: Deep research agents can consume significant tokens (e.g., >500k tokens per run), increasing costs (Context Engineering).
  • Coordination in Multi-Agent Systems: Ensuring seamless interaction among agents requires careful context isolation, such as using schemas or sandbox environments.

Relevant Research from arXiv

Recent research on arXiv provides valuable insights into context engineering:

  • Data Engineering for Scaling Language Models to 128K Context:

    • Findings: Lightweight continual pretraining with 500M to 5B tokens of balanced, domain-diverse data enables LLMs to handle 128K-token contexts. Domain balance and length upsampling are critical for optimal performance.
    • Implication: Context engineering for long contexts requires careful data engineering to maintain model capabilities across extended inputs.
    • Code: Available at Long Context Data Engineering.

  • ContextCite: Attributing Model Generation to Context:

    • Findings: ContextCite is a scalable method to pinpoint which context parts influence specific model outputs, aiding in verification, context pruning, and poisoning detection.
    • Implication: Tools like ContextCite enhance context engineering by ensuring outputs are grounded in relevant context, improving reliability.
    • Code: Available at ContextCite GitHub.

Additional Insights from Industry

  • Organizational Context: Ethan Mollick on X emphasizes that context engineering involves aligning LLMs with organizational operations, including reports, processes, and tone (Mollick's Post). This cross-functional approach ensures AI outputs reflect company identity.
  • Tool Integration: Tools like LangGraph and LangSmith facilitate context engineering by providing control over agent workflows and observability (LangGraph, LangSmith).
  • Community Perspectives: X posts highlight diverse views, such as @virtualmilin’s use of unique code and app flow context for a 95% success rate in root cause analysis (Virtualmilin's Post), and @ankrgyl’s note on the pervasive challenge of engineering every stack layer (Ankrgyl's Post).

Practical Example Of Context Engineering Workflow

Objective

Create an LLM-based email assistant that responds to customer inquiries with relevant product information.

Context Components
  • User Preferences: Stored in a JSON file, updated based on feedback. 
    {
  "user_id": "123",
  "preferred_tone": "professional",
  "language": "English"
}
  • Product Data: Retrieved via RAG from a product database. 
    {
  "product_id": "456",
  "name": "Widget X",
  "features": ["Feature A", "Feature B"],
  "price": "$99.99"
}
  • Conversation History: Summarized to maintain context continuity. 
    Customer asked about Widget X features on 2025-06-25.
  • Instructions: Clear, structured prompt. 
    You are a professional email assistant. Respond to the customer's inquiry about Widget X using the provided product data and user preferences. Maintain a professional tone.
Workflow
  1. Retrieve Context: Fetch user preferences and product data.
  2. Summarize History: Compress conversation history to fit context window.
  3. Format Prompt: Combine instructions, preferences, data, and history in a structured format.
  4. Evaluate: Check if the context enables a plausible response; adjust if necessary.
  5. Generate Response: Pass the context to the LLM for response generation.
Expected Output

A professional email response detailing Widget X’s features, tailored to the user’s preferences.

Conclusion

Context engineering is a multifaceted discipline that enhances LLM performance by ensuring the right information, tools, and formats are provided. By implementing best practices like instrumentation, structured state management, and dynamic context assembly, developers can create robust AI applications. Ongoing research and tools like LangGraph and ContextCite continue to advance the field, addressing challenges like token costs and context degradation. As LLMs evolve, context engineering will remain a cornerstone of effective AI development, bridging technical precision with organizational needs.

Key Citations: