For two years, the AI industry has treated prompt engineering as a skill, a discipline, even a job title. LinkedIn profiles declare “Senior Prompt Engineer.” Courses promise to teach the art and science of talking to language models. And the advice follows a familiar pattern: be specific, use few-shot examples, assign a persona, chain your reasoning.
This advice is not wrong. It is incomplete, and it is aging out of relevance.
The bottleneck for useful AI applications was never the prompt. It was everything surrounding the prompt: what information is available, how it is structured, what constraints are applied, what examples are selected, and how the entire context window is composed. This is context engineering, and it is a systems discipline, not a copywriting exercise.
At CONFLICT, we made this shift internally about 18 months ago. We stopped asking “how should we prompt this?” and started asking “what does the model need to know to do this well?” The change in framing changed everything about how we build AI systems.
The Limits of Prompt Engineering
Prompt engineering treats the prompt as the primary lever for controlling LLM behavior. This made sense in the early days of ChatGPT when the prompt was essentially the only input. You typed a question, the model answered. Better questions got better answers.
But modern AI systems are not chat interfaces. They are pipelines where the LLM is one component among many. The prompt is constructed programmatically from multiple sources: system instructions, retrieved documents, conversation history, tool results, examples, constraints, and user input. The hand-crafted prompt is a small fraction of what the model actually sees.
In this context, the classic prompt engineering techniques have diminishing returns:
“Be specific in your instructions” helps, but if the model does not have the right information in its context, specificity in the instructions cannot compensate. Telling a model to “analyze the customer’s billing history” is useless if the billing history is not in the context window.
“Use few-shot examples” is powerful, but which examples? Static examples work for static tasks. For dynamic tasks where the input varies widely, you need dynamic example selection – and that is a retrieval problem, not a prompting problem.
“Assign a persona” shapes the response style but not the response accuracy. A model adopting the persona of a tax expert does not actually know tax law. The persona instruction needs to be backed by actual tax knowledge in the context.
“Chain of thought” improves reasoning for complex problems, but the quality of the reasoning depends on the quality of the information being reasoned about. Chain of thought on bad data produces well-reasoned wrong answers.
The pattern is clear: every prompt engineering technique is limited by the quality and relevance of the context in which it operates. Context engineering addresses the fundamental constraint.
What Context Engineering Is
Context engineering is the discipline of designing, constructing, and managing the full context window that a language model receives. It encompasses:
Information selection. What information does the model need for this specific task? This is the retrieval problem – finding the right documents, records, examples, and data to include. RAG is one implementation of information selection, but it is not the only one. Sometimes the right information comes from a database query, an API call, or a structured knowledge graph traversal.
Information structuring. How should that information be organized within the context window? The order, format, and structure of context material affects how the model processes it. Information at the beginning and end of long contexts gets more attention than information in the middle. Structured formats (tables, JSON, XML) are processed differently than prose. Headers and sections create navigation cues.
Constraint specification. What boundaries should the model operate within? Output format requirements, domain boundaries, safety constraints, and behavioral guidelines. These are part of the system prompt, but they also depend on the task context – constraints for a customer-facing response differ from constraints for an internal analysis.
Example curation. What examples best illustrate the desired behavior for this specific input? Dynamic few-shot selection – choosing examples that are most similar to the current input – dramatically outperforms static examples. This is another retrieval problem: maintaining an example store and selecting relevant examples at inference time.
History management. For conversational or multi-step tasks, what prior context should be retained and what can be summarized or dropped? Context windows are finite. Managing conversation history to preserve important context while staying within token limits is an engineering challenge that grows with interaction length.
class ContextBuilder:
def __init__(self, max_tokens: int = 128000):
self.max_tokens = max_tokens
self.components = []
def add_system_instructions(self, instructions: str, priority: int = 1):
self.components.append({
"type": "system",
"content": instructions,
"priority": priority,
"tokens": count_tokens(instructions)
})
def add_retrieved_context(self, documents: list, priority: int = 2):
for doc in documents:
self.components.append({
"type": "context",
"content": doc.text,
"metadata": doc.metadata,
"priority": priority,
"tokens": count_tokens(doc.text)
})
def add_examples(self, examples: list, priority: int = 3):
for ex in examples:
self.components.append({
"type": "example",
"content": format_example(ex),
"priority": priority,
"tokens": count_tokens(format_example(ex))
})
def build(self) -> str:
# Sort by priority, then fit within token budget
sorted_components = sorted(self.components, key=lambda x: x["priority"])
selected = []
remaining_tokens = self.max_tokens
for component in sorted_components:
if component["tokens"] <= remaining_tokens:
selected.append(component)
remaining_tokens -= component["tokens"]
return self._assemble(selected)
Context Window as a Design Space
The context window is not just a text field – it is a design space with structure, constraints, and optimization opportunities.
Positional effects. Research and practice confirm that LLMs pay more attention to information at the beginning and end of the context window than to information in the middle. For long contexts, place the most important information (key instructions, critical constraints, the most relevant retrieved document) at the boundaries. Place supporting information in the middle.
Format effects. The format of context material matters. Structured data (JSON, XML, tables) is parsed differently than prose. For factual retrieval tasks, presenting source material in structured format with clear labels (“Source: HR Policy v3.2, Section 4.1”) improves citation accuracy. For analytical tasks, prose context with clear argumentation supports better reasoning.
Density effects. Dense context (packed with information, minimal redundancy) is processed differently than sparse context. Very dense context can overwhelm the model’s attention, causing it to miss important details. Strategic use of white space, headers, and section breaks improves processing of long contexts.
Interference effects. Contradictory information in the context window causes problems. If two retrieved documents disagree, the model may blend them, pick one arbitrarily, or hallucinate a reconciliation. Context engineering includes detecting and resolving contradictions before they reach the model.
These effects are not theoretical – they directly impact output quality in production systems. We have seen cases where simply reordering context components (moving the most relevant retrieved document from position 5 to position 1) improved answer accuracy by double-digit percentages.
Dynamic Context Construction
The shift from prompt engineering to context engineering is fundamentally a shift from static to dynamic. A prompt engineering approach writes a prompt template and fills in the user’s question. A context engineering approach constructs a unique context for each request based on the specific input, the available information, and the task requirements.
This means building infrastructure:
Retrieval systems that find relevant information from your knowledge base, databases, APIs, and knowledge graphs. The quality of your retrieval directly determines the quality of your context. Invest here.
Example stores with dynamic selection. Maintain a curated set of input-output examples for each task type. At inference time, select the examples most similar to the current input using embedding similarity or other matching criteria. Three relevant examples outperform twenty irrelevant ones.
Context assembly pipelines that combine components from multiple sources into a coherent context window. These pipelines need to handle token budgeting (staying within limits), deduplication (not including the same information twice from different sources), and formatting (consistent structure across components).
Context evaluation that measures whether the assembled context contains the information needed to produce a good output. This is the gap between “we retrieved 5 documents” and “the retrieved documents contain the answer to the user’s question.” Measuring this gap is how you improve your context engineering iteratively.
class ContextEvaluator:
def evaluate(self, query: str, context: str, expected_answer: str) -> dict:
# Check if context contains information needed for the answer
coverage = self._measure_information_coverage(context, expected_answer)
# Check for contradictions in context
contradictions = self._detect_contradictions(context)
# Estimate context relevance
relevance = self._score_relevance(query, context)
return {
"coverage": coverage,
"contradictions": contradictions,
"relevance": relevance,
"token_usage": count_tokens(context),
"token_efficiency": coverage / max(count_tokens(context), 1)
}
Context Engineering in Practice
Let me walk through how context engineering works in a real system we built: an internal knowledge assistant for a client with thousands of policy documents, technical specifications, and process guides.
The prompt engineering approach (what we tried first): Write a detailed system prompt explaining the assistant’s role, include instructions for handling different question types, add a few static examples, and use RAG to retrieve relevant documents. This worked for simple factual questions and failed for everything else – nuanced policy questions, comparative analyses, questions that spanned multiple documents.
The context engineering approach (what we shipped):
For each user query, the system:
Classifies the query type (factual lookup, policy interpretation, comparison, procedure, troubleshooting). Different query types need different context compositions.
Retrieves from multiple sources. Policy documents via semantic search. Related Q&A pairs from the example store. Relevant metadata from the knowledge graph (document ownership, update history, related policies). The retrieval strategy varies by query type.
Selects examples dynamically. From a curated set of 200+ Q&A examples, selects the 3 most relevant to the current query. These examples demonstrate the expected response format, depth, and citation style for similar questions.
Assembles context with structure. The context window is organized into sections: system instructions, relevant examples, primary source documents (with metadata), supporting context, and the user query. Section headers and clear delineation help the model navigate the context.
Applies query-type-specific constraints. Policy interpretation queries include instructions to cite specific section numbers. Comparison queries include instructions to present a balanced analysis. Procedure queries include instructions to provide step-by-step format.
Evaluates context quality. Before sending to the LLM, a fast evaluation checks whether the retrieved documents are likely to contain the answer. If confidence is low, the system retrieves additional context or flags the query for human handling.
The result: answer accuracy improved from 71% to 89% compared to the prompt engineering approach. The improvement came entirely from better context – the LLM, the system prompt, and the generation parameters were identical.
The Infrastructure Investment
Context engineering requires infrastructure that prompt engineering does not:
A retrieval stack. Vector databases, keyword indexes, re-rankers. This is the RAG infrastructure, but context engineering goes beyond basic RAG by combining multiple retrieval strategies and sources.
An example management system. A curated, searchable store of input-output examples with embedding-based similarity search. Examples need to be maintained: added when new patterns emerge, removed when they become outdated, updated when desired behavior changes.
A context assembly layer. Code that composes the final context from retrieved components, applying token budgets, formatting, and structural rules. This is the “prompt template” replacement – instead of a static template with a few variables, it is a dynamic assembly pipeline.
Evaluation infrastructure. Test sets with queries, expected contexts, and expected outputs. Automated evaluation that runs regularly to detect quality regressions. Metrics that distinguish context quality from generation quality so you know where to focus improvements.
This is real engineering work. It is more complex than writing prompt templates. But it produces systems that are more robust, more maintainable, and more predictable. A prompt template is a single point of failure – one bad prompt ruins every output. A context engineering system is modular – a bad retrieval component can be fixed independently of the generation component.
The Organizational Shift
The shift from prompt engineering to context engineering also changes who does the work and how they collaborate.
Prompt engineering can be done by a single person iterating on text. Context engineering requires collaboration between:
- Domain experts who know what information is relevant for different query types
- Data engineers who build and maintain the retrieval infrastructure
- ML engineers who select and tune embedding models and re-rankers
- Product managers who define the expected behavior and quality standards
- Software engineers who build the assembly pipeline and integration points
This is not a one-person job. It is a systems engineering effort that spans disciplines. At CONFLICT, this is core to how we practice what we call Outcome Engineering – starting from the desired outcome and engineering the full system to produce it, rather than tweaking individual components in isolation.
The era of the lone prompt engineer iterating on text in a playground is ending. The era of context engineering teams building information systems that feed language models is beginning. The models will keep getting better. The competitive advantage will be in the systems that surround them – the context engineering that determines whether those models produce generic responses or genuinely useful, grounded answers.
Prompt engineering was a starting point. Context engineering is the discipline.
