Article Series: An Introduction to AI in context of business

  1. What are AI Agents ↗
  2. AI 'Brains': Large Language Models ↗
  3. Prompt Engineering for Business ↗
  4. Tool-Enabled AI Systems ↗
  5. Retrieval-Augmented Generation (RAG) ↗
  6. AI Memory Systems ↗
  7. Context Engineering

Context engineering is the strategic practice of structuring, prioritising, and managing all information provided to AI systems to achieve optimal, reliable results. Whilst prompt engineering focuses on crafting individual instructions, context engineering takes a broader view - it's about orchestrating the entire information environment in which your AI operates.

Think of it as setting the stage for a performance. Not only are you telling the actor what to say (the prompt), you're also managing the script, the props, the lighting, the audience brief, and even what happened in previous acts.
Get this right, and your AI performs consistently and reliably.
Get it wrong, and even the best prompts produce disappointing results.

For businesses deploying AI agents or Large Language Model (LLM) applications, context engineering represents the difference between a system that occasionally impresses and one that delivers dependable value day after day.

Why Context Engineering Matters

LLMs process information within what's called a "context window" - essentially, their working memory for a single interaction. Everything the model considers when generating a response must fit within this window: your instructions, conversation history, retrieved documents, examples, and the user's actual question. Filling that space strategically is what differentiates expert AI design from random prompting.

Here's the challenge: modern LLMs have context windows ranging from 8,000 to over 1 million tokens (roughly 6,000 to 750,000 words).

```A token is a fundamental unit of text that a LLM uses to process and generate human language. 
LLMs do not "read" text as humans do; instead, they convert text into numerical representations of tokens, which they then process.```

That sounds generous until you realise that a typical business scenario might involve:

  • System Prompt: System instructions defining the AI's role and constraints
  • RAG: Retrieved documents from your AI knowledge base
  • Short-Term Memory: Conversation history showing what's been discussed
  • Long-Term Memory: User memory, preferences and past interactions
  • Tools Output: Structured result outputs from tool use
  • User Prompt: The actual user query
  • User/System Prompt: Few-shot examples demonstrating desired behaviour

Suddenly, that generous context window feels rather cramped. Worse, research shows that LLMs don't treat all information in their context equally - they tend to pay more attention to information at the beginning and end, sometimes "losing" crucial details buried in the middle (a phenomenon researchers call the "lost in the middle" problem).

Poor context engineering leads to inconsistent outputs, hallucinations (the AI inventing information), failure to follow instructions, and ultimately, business users losing trust in the system.

Effective context engineering ensures your AI has the right information, in the right order, at the right time.

Business Context

The business impact of context engineering becomes apparent when you consider real-world applications. A customer service AI that loses track of the conversation context might ask customers to repeat information they've already provided. A legal document analysis system that can't properly prioritise context might miss critical clauses. A sales assistant that doesn't maintain user preferences across sessions creates a frustrating, impersonal experience.

Context engineering directly influences:

  • Consistency: Well-engineered context produces predictable, reliable outputs that business users can depend upon
  • Accuracy: Properly prioritised context reduces hallucinations and ensures the AI focuses on relevant information
  • Cost: Efficient context management reduces token consumption, directly lowering API costs (particularly important when processing large volumes)
  • User experience: Maintaining appropriate context across conversations creates natural, flowing interactions
  • Compliance: Structured context engineering helps ensure the AI adheres to regulatory requirements and company policies

Companies implementing effective context engineering strategies typically report 30-50% reductions in error rates and similar improvements in user satisfaction scores. More importantly, they build AI systems that scale reliably as usage grows.

Understanding Context Components

To engineer context effectively, you need to understand the different types of information you're managing:

System Prompts vs User Prompts

System prompts establish the AI's role, personality, and operational constraints. They're set by you, the developer, and remain consistent across interactions. User prompts are the actual questions or instructions from end users. The distinction matters because most LLMs treat these differently - system prompts often receive higher priority in the model's decision-making.

A well-crafted system prompt for a business application might include:

  • Role definition ("You are a financial analyst assistant...")
  • Core constraints ("Never provide specific investment advice...")
  • Output formatting requirements
  • Tone and communication style
  • Error handling instructions

Retrieved Context (RAG)

When implementing RAG systems, you're injecting relevant documents or data into the context. The challenge is selecting which chunks of information to include and how to present them. Including too much creates noise; including too little risks missing crucial information.

Conversation History

For multi-turn conversations, you need to maintain relevant history so the AI understands references like "the document we discussed earlier" or "as I mentioned before". However, including complete conversation history quickly exhausts your context window and adds unnecessary cost.

Memory and State

User preferences, previous interactions, and learned information need to be selectively included when relevant. A customer service AI should remember that a user prefers email communication, but doesn't need that information when the user is asking about product specifications.

Examples and Demonstrations

Few-shot learning examples that demonstrate desired behaviour can be powerful, but they consume significant context space. Choosing which examples to include, and when, requires strategic thinking.

Benefits

  • Improved Accuracy: Properly engineered context reduces hallucinations and ensures responses are grounded in relevant information
  • Consistent Performance: Strategic context structuring produces more reliable, predictable outputs
  • Better User Experience: Appropriate context maintenance creates more natural, flowing conversations
  • Compliance Support: Structured context injection helps ensure the AI operates within defined constraints and policies

Challenges

  • Complexity: Balancing multiple context sources requires careful orchestration and ongoing tuning
  • Token Limitations: Despite large context windows, complex business scenarios can still exceed practical limits
  • Cost vs Quality Trade-offs: More context generally improves quality but increases costs - finding the optimal balance requires experimentation
  • Testing Difficulty: Context engineering decisions are often subtle, making it challenging to establish clear testing criteria
  • Model Variability: Different LLMs handle context differently, requiring adjustments when changing providers

Examples in Practice

Customer Service Application

A telecommunications company implemented context engineering for their AI customer service agent. Their system:

  • Maintains the last 5 conversation turns in full detail
  • Summarises older conversation history beyond those 5 turns
  • Retrieves relevant help articles based on the current issue (top 3 matches only)
  • Includes customer account status and preferences from their CRM
  • Prioritises system instructions that prevent the AI from making unauthorised account changes

Result: 45% reduction in repeat questions from customers and 30% improvement in first-contact resolution.

Legal Document Analysis

A law firm built an AI system for contract review that uses sophisticated context layering:

  • System prompt defines legal standards and review criteria
  • Includes relevant precedent examples (2-3 most similar cases)
  • Injects the contract sections being reviewed
  • Adds specific client requirements and risk tolerance
  • Maintains notes from previous contract sections for consistency

By carefully prioritising which precedents and which sections of the contract to include, they reduced their context usage by 60% whilst improving review consistency.

Sales Assistant

A B2B software company deployed an AI sales assistant that manages context across weeks of customer interactions:

  • Maintains a persistent profile of each prospect (industry, pain points, decision-makers)
  • Summarises previous conversations after each session
  • Dynamically retrieves relevant product information based on the current discussion
  • Tracks open questions and action items across sessions
  • Adjusts tone based on sales stage (early exploration vs late-stage negotiation)

Their sales team reported that the AI "remembers conversations better than we do" and conversion rates improved by 25%.

How Companies Can Incorporate Context Engineering

Start with these practical steps:

  • Audit Your Current Context
    Examine what information you're currently providing to your AI systems. Log actual context sizes and identify what's being included. You'll often discover significant waste - information being included that the AI never uses.

  • Establish Token Budgets
    Define maximum token allocations for different context components. This forces disciplined prioritisation and prevents any single source from dominating.

  • Implement Context Monitoring
    Track context window utilisation, token consumption patterns, and correlation with output quality. This data guides optimisation efforts.

  • Test Context Variations
    Run A/B tests with different context structures. Try varying the amount of retrieved information, conversation history, or example inclusion. Measure impact on accuracy, cost, and latency.

  • Build Summarisation Pipelines
    For applications with ongoing conversations, implement automatic summarisation of older content. This maintains continuity whilst managing context size.

  • Create Context Templates
    Develop standard templates for common scenarios. A customer query might use one context structure, whilst a document analysis uses another. Templates ensure consistency and simplify maintenance.

  • Implement Progressive Enhancement
    Start with minimal context and progressively add more only when needed. If the AI can answer accurately with limited context, don't waste tokens on additional information.

Summary and Next Steps

Context engineering turns AI from a clever toy into a business tool. It’s not about feeding more text into the model but feeding the right information in the right order.

The discipline requires thinking beyond individual prompts to orchestrate the entire information environment in which your AI operates. It demands understanding of token economics, LLM attention patterns, and the specific requirements of your business use case.

Start small: audit your current context usage, establish basic prioritisation rules, and implement monitoring. As you gain experience, develop more sophisticated strategies for context layering, dynamic adjustment, and cross-session management.

The next article in this series covers AI Orchestration and Workflows - how to coordinate multiple AI operations, manage complex business processes, and build reliable, multi-step AI applications. If context engineering is about managing information within a single AI interaction, orchestration is about managing the flow of work across multiple interactions and systems.

Links and Further Reading

** Notes

  • The article was drafted by an AI Agent.
  • Then reviewed by a human and published - (Us here at Siris!)
  • The topic research and guidance on style, tone and audience were built into the agent.