Aditya Lahiri is the Co-Founder & CTO of OpenFunnel, a Y Combinator-backed company pioneering AI-powered GTM.
As large language models (LLMs) become increasingly sophisticated, a new discipline is emerging that goes far beyond traditional prompt engineering: context engineering. This evolving practice represents the art of providing comprehensive, multisourced context to enable AI systems to solve complex business problems with unprecedented accuracy and relevance. Context engineering embodies the maturation of AI from a tool that responds to instructions into a strategic partner that understands, reasons and acts within the full complexity of business environments through multiple input data sources, both external and internal.
Understanding Context Engineering
While prompt engineering focuses on crafting the right instructions for a single interaction with an AI model, context engineering takes a fundamentally different approach. It’s not a one-step process, nor is it isolated to a single data source. Instead, context engineering is a systematic methodology that gathers, processes and synthesizes information from multiple sources across extended workflows to create the most informed foundation for AI decision making.
Traditional prompt engineering is like giving someone directions to a destination. Context engineering is like being their travel agent, navigator and local guide all at once, providing not just the route but understanding their preferences, constraints, real-time conditions and optimal timing.
The distinction is crucial: Prompt engineering optimizes individual queries while context engineering optimizes entire decision-making processes by ensuring AI systems have access to the full spectrum of relevant information needed to generate truly impactful outcomes.
Context Engineering In Go-To-Market Strategies
Context engineering has been transformative for AI-enabled go-to-market (GTM) strategies. Traditional AI-powered sales development representatives (SDRs) have long relied on basic prompt engineering—essentially, a vanilla approach of identifying prospects and crafting generic outreach messages based on limited data points.
The typical workflow looks like this: “I saw you work at Company XYZ as a Sales Director” → generate message → send email. Although this represents an improvement over manual processes, it barely scratches the surface of what’s possible with comprehensive context engineering.
The Phases Of Context Engineering
Advanced context engineering in GTM strategies involves a sophisticated, multilayered approach that transforms how sales teams identify, research and engage prospects. This methodology operates through several distinct phases:
Signal Trigger: AI agents continuously monitor the internet for companies and individuals matching specific GTM goals. These systems employ deep reasoning capabilities to identify prospects who aren’t just demographically aligned but are actively demonstrating buying signals or market readiness. Every finding includes public references and verification, ensuring the foundation of context is both accurate and actionable.
Research Enrichment: Once prospects are identified, specialized research agents dive deeper using tools like LinkedIn Sales Navigator, browser automation and comprehensive internet analysis. These agents answer hyperspecific qualifying questions and understand company dynamics, recent changes and market positioning that could influence buying decisions.
System Integration: The most sophisticated aspect involves two-way synchronization with existing systems of record. This means AI doesn’t just push context into CRM systems. It learns from historical interactions, deal statuses, previous conversations and relationship history to inform the outreach strategies.
Temporal Optimization: Armed with comprehensive internal and external context, the system determines not just what to say but when to say it. This timing intelligence considers market conditions, company events, seasonal factors and individual prospect behavior patterns.
The Future Of Context Engineering
As AI reasoning capabilities continue advancing, context engineering will likely expand beyond GTM strategies into other complex business domains requiring multisource intelligence synthesis. The principles—comprehensive data gathering, intelligent processing and strategic application—have potential in everything from customer success to product development.
The move from prompt engineering to context engineering represents more than a technical advancement; it’s a fundamental shift toward AI systems that understand and operate within the full complexity of business contexts rather than isolated interactions.
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