Context Engineering 101
Context Engineering is the systematic design of the entire information environment for a Large Language Model (LLM). It moves beyond simple "prompting" to architecting dynamic systems that provide the right information, tools, and memory to an LLM, setting it up for success. This interactive guide explores the core techniques and architectures that form this critical AI discipline.
The Core Analogy: LLM as an OS
Think of an LLM as a CPU. Its context window is its RAM—a finite, temporary workspace. Context Engineering is the "operating system" that manages what gets loaded into this RAM for each task. Its primary challenges are selecting the most relevant information and formatting it to fit within the context window's limits.
Category I: Context Retrieval & Generation
This foundational category covers techniques for sourcing the raw information that populates the context window. This can be done by guiding the LLM to generate its own reasoning (In-Context Learning) or by fetching information from external knowledge bases.
Zero-Shot Prompting
Instructing an LLM to perform a task without providing any examples, relying on its pre-trained knowledge.
Mechanism
A prompt is constructed with only the task description and the input. The model must infer the requirements and generate a response based on its implicit understanding.
Examples
> Classify the sentiment of the following statement: 'The movie was fantastic, and I would watch it again!'> Translate the following English text to French: 'Hello, how are you?'> Summarize the following paragraph into a single sentence: Artificial intelligence is transforming industries by automating tasks, providing data-driven insights, and enabling personalized customer experiences. It is a field with vast potential for innovation and growth across various sectors.> Write a Python function that takes a list of numbers and returns the sum.> Extract the main keywords from this text: 'Artificial intelligence is transforming industries by automating tasks and providing data-driven insights.'Few-Shot Prompting
Providing the LLM with a few examples (or "shots") of the task to guide its output format and style.
Mechanism
The prompt includes a set of example input-output pairs before the final input. The model learns the pattern from these examples and applies it.
Examples
> Classify the text into neutral, negative or positive.
>
> Text: I think the vacation is okay.
> Sentiment: neutral
>
> Text: I think the food was okay.
> Sentiment:> Extract user info as JSON.
>
> Input: John Doe is 30 years old.
> Output: {"name": "John Doe", "age": 30}
>
> Input: Jane Smith is 25.
> Output:> Convert to camelCase.
>
> snake_case_variable -> snakeCaseVariable
> another_example_string -> anotherExampleString
> my_python_var ->> A conversation between a user and a bot.
>
> User: Hi!
> Bot: Hello! How can I help you today?
>
> User: I need to check my order status.
> Bot:> Extract the company name.
>
> Text: Apple announced the new iPhone.
> Company: Apple
>
> Text: Microsoft released Windows 11.
> Company:Chain-of-Thought (CoT) Prompting
Prompting the LLM to generate a step-by-step reasoning process before giving the final answer.
Mechanism
The model is encouraged to "think out loud" by providing examples of step-by-step reasoning or by simply adding the phrase "Let's think step-by-step." The generated reasoning becomes part of the context, scaffolding the final answer.
Examples
> Q: Leah had 32 chocolates and her sister had 42. If they ate 35, how many pieces do they have left in total?
> A: Originally, Leah had 32 chocolates. Her sister had 42. So in total they had 32 + 42 = 74. After eating 35, they had 74–35 = 39. The answer is 39.
>
> Q: Shawn has five toys. For Christmas, he got two toys each from his mom and dad. How many toys does he have now?
> A:> Q: If Tom is taller than Sam, and Sam is taller than Alex, who is the tallest?
> A: Let's break it down. We are given two facts: 1. Tom > Sam. 2. Sam > Alex. From this, we can infer that Tom > Sam > Alex. Therefore, Tom is the tallest.
>
> Q: A book, a pen, and a cup are on a table. The pen is to the left of the cup. The book is to the right of the pen. What is the order?
> A:> Q: I need to get from my house to the airport. What are the steps?
> A: Let's think step-by-step. First, I need to check the flight time. Second, I should book a taxi or check the bus schedule. Third, I need to pack my bags. Fourth, I should leave for the airport, allowing for traffic. Finally, I will check in and go through security.
>
> Q: I want to bake a cake. What do I need to do?
> A:> Q: This Python code `sum([1, '2'])` gives an error. Why?
> A: Let's analyze the code. The `sum()` function tries to add elements of a list. The list contains an integer `1` and a string `'2'`. The `+` operator is not defined between an integer and a string in Python. This causes a TypeError.
>
> Q: My JavaScript `console.log(0.1 + 0.2)` prints `0.300...4`. Why?
> A:> Q: Is the Eiffel Tower in Rome?
> A: Let's check the facts. The Eiffel Tower is a famous landmark. I know it is located in Paris, France. Rome is the capital of Italy. Therefore, the statement that the Eiffel Tower is in Rome is false.
>
> Q: Is water made of two hydrogen atoms and one oxygen atom?
> A:Tree-of-Thoughts (ToT) Prompting
Enabling the LLM to explore multiple parallel reasoning paths and backtrack from unpromising ones.
Mechanism
ToT treats reasoning as a search over a tree. It involves decomposing the problem, generating multiple potential next steps ("thoughts"), having the LLM evaluate the promise of each thought, and then exploring the most promising paths.
Conceptual Example: Game of 24
Given numbers 4, 9, 10, 13, the model generates multiple first steps like `(10 - 4 = 6)` or `(9 + 4 = 13)`. It evaluates each, deems `(6, 9, 13)` as 'likely' to succeed, and expands that path further, exploring possibilities like `(9 - 6 = 3)`.
Graph-of-Thoughts (GoT) Prompting
Allowing the LLM to merge and cycle through reasoning paths in a graph structure for more complex problem-solving.
Mechanism
GoT generalizes ToT by allowing thought paths to be merged (aggregation) and iteratively improved (refinement). This allows for combining insights from different reasoning lines and enhancing solutions through feedback loops.
Conceptual Example: Sorting
A GoT approach can mimic merge-sort. It splits the list into sub-lists (branching), sorts them in parallel, and then merges the sorted lists back together (aggregation) to produce the final result.
Visualizing Reasoning Structures
A linear sequence of thoughts.
Retrieval-Augmented Generation (RAG)
Retrieving relevant information from an external knowledge base to augment the prompt and ground the LLM's response in facts.
Mechanism
When a user asks a question, the system first retrieves relevant documents from a knowledge source (like a vector database). These documents are then added to the context along with the original question, and the LLM is instructed to answer based on the provided information.
RAG Workflow
Category II: Context Processing & Optimization
Once context is sourced, it must be refined and transformed to be maximally effective. These techniques focus on handling long sequences, enabling self-correction, and integrating diverse data formats to improve the quality and relevance of the context.
Long-Context Processing
Using architectural innovations to efficiently handle very long input sequences, like entire documents or codebases.
Mechanism
This involves either architectural changes (like FlashAttention or State-Space Models) to improve computational efficiency, or procedural changes to mitigate issues like the "Lost in the Middle" problem, where models struggle to recall information from the center of a long context.
"Needle-in-a-Haystack" Strategy
To combat the "Lost in the Middle" issue, the most critical information (the "needle") should be placed at the very beginning or, more effectively, at the very end of the long context (the "haystack"), just before the final instruction.
Contextual Self-Refinement
Using the LLM to iteratively critique and improve its own outputs without extra training data.
Mechanism
The process is a loop: 1) The LLM generates an initial output. 2) The LLM is asked to provide feedback on its own output. 3) The LLM is given the original prompt, its first output, and its own feedback, and is asked to generate a refined version. This can be repeated multiple times.
Refinement Workflow
Structured Context Integration
Providing context in structured formats like JSON, XML, or tables for better parsing and reasoning.
Mechanism
Structured data is included directly in the prompt, often with delimiters. The explicit schema reduces ambiguity and allows the model to more reliably perform tasks like querying data or interacting with APIs.
Example: SQL Generation
> """
> Table: students
> Columns: student_id (INT), student_name (VARCHAR), department_id (INT)
>
> Table: departments
> Columns: department_id (INT), department_name (VARCHAR)
>
> Create a MySQL query for all students in the 'Computer Science' department.
> """Category III: Context Management & Persistence
The LLM's context window is finite and temporary. These techniques focus on managing this limited resource and persisting important information across interactions, which is crucial for building stateful and efficient applications.
Context Compression & Summarization
Reducing the token count of context while preserving its core semantic meaning to fit within the context window.
Mechanism
The most common method is to use an LLM to summarize long pieces of text. For example, in a long conversation, the history can be periodically summarized, and this shorter summary replaces the verbose original text in the context for future turns.
Memory Architectures
Mechanisms for storing and retrieving information beyond a single interaction, enabling personalization and learning.
Short-Term Memory (Scratchpads)
A temporary storage area (like a variable) where an agent can write down its plan or intermediate results for the current task. This helps maintain state even if parts of the early context are trimmed.
Long-Term Memory (Vector DBs)
An external database (commonly a vector database) is used to store important information across sessions. Facts are converted to numerical embeddings and stored. In later sessions, the system can retrieve relevant memories via similarity search to personalize the interaction.
System Implementations
These are higher-level architectural patterns that integrate the foundational components into coherent, functional systems like AI agents and advanced question-answering applications.
Advanced RAG Systems
Architectures that add more logic to the basic RAG pattern, such as re-ranking retrieved documents or using an agent to control the retrieval process.
Tool-Integrated Reasoning
Systems that enable LLMs to use external tools (like APIs or code execution) via function calling, allowing them to interact with the outside world.
Mechanism: Function Calling
The LLM is provided with definitions of available tools. When needed, it generates a structured JSON object specifying the tool to call and the parameters to use. An external runtime executes the tool and returns the result to the LLM's context.
Multi-Agent Systems
Workflows that orchestrate multiple specialized agents to solve a complex problem, following a "divide and conquer" approach.
Mechanism
A primary "orchestrator" agent breaks down a large goal into sub-tasks and delegates them to a team of specialized "worker" agents. Each worker has its own focused context and tools, improving efficiency and reliability. For example, one agent could handle web search while another handles data analysis.