Context Engineering

Arvin Liu @ RayAegis

Context Engineering vs Prompt Engineering
AI Agent Basics
Prompt Engineering - How to adjust your prompt?
Reduce Hallucination
Strategy

Schedule

Prompt v.s Context Engineering

What is the target?

Prompt

Answer

Fix the prompt

Gemini

Fine-tune?

💬

💡

What is Prompt Engineering?

Use Magic Spell

Emotional Prompt

Structure Preserving

<default_to_action>
By default, implement changes rather than only suggesting them. If the user's intent is unclear, infer the most useful likely action and proceed, using tools to discover any missing details instead of guessing. Try to infer the user's intent about whether a tool call (e.g., file edit or read) is intended or not, and act accordingly.
</default_to_action>

Zero-shot-CoT

Chain-of-Thought

What is Context Engineering?

Sometimes an LLM fails not because of the prompt, but due to its limitations.
- Requires tool calls (e.g., calculations, Google Workspace
- Requires up-to-date knowledge (RAG)

Conclusion

Prompt / Context Engineering ≠ Changing model parameters

Goal: Make the model perform better through better prompts.

AI Agent Basics

AI Agent Basics - LLM Encoding

Encoders used in modern LLMs

Modern LLMs use self-attention.
→ Tokens reference previous tokens in the context.

→ Strong imitation and pattern-following ability.

AI Agent Basics - LLM Encoding

Decoding process:
Generate token → Append to context → Repeat

→ That's why Role prompts can guide generation.

AI Agent Basics - LLM Decoding

Modern LLMs often use MoE (Mixture of Experts).
A router model selects the appropriate expert.

→ Clear and simple prompts may help improve routing decisions.

Mixture of Experts

AI Agent Basics - MoE

What you do in multi-round conversations,

reasoning models also perform internally.

Some models support Chain-of-Thought (CoT) reasoning and self-correction.

Thinking Model

AI Agent Basics - Reasoning Model

Thinking Model

AI Agent Basics - Reasoning Model

Reasoning Model in ChatGPT

Knowing the Basics...

Let's rethink our prompts!

Prompt Engineering

How Anthropic, OpenAI, Google Use Prompt Engineering

Prompt Engineering Guide

Instruction Clarity
Context and Task Definition
Output Control
Guide the thinking

Prompt Engineering Guide

疊甲聲明

Instruction Clarity

Treat LLM as your colleague

1. Instruction Clarity

You

Co-worker

Assume you need to assign work to a very smart colleague.

They are capable, but don’t know what you want.
Give clear, detailed instructions.

That’s exactly how an LLM wants to be treated.

Give me a hand to write some code

✋

???

Be Explicit About the Task
Define the Task in Detail
Define Input Type

1. Instruction Clarity

Be Explicit About the Task
- Clearly state the required action
- Avoid vague terms (e.g., “some”, “fairly short”)
- Specify the output format (Markdown / XML / JSON)

1. Instruction Clarity

Give me some notable CVE.

✅

❌

Give me 5 CVE events with CVSS score >= 9.

* Vulnerability Name: The common name (e.g., Log4Shell).
* Impacted Systems: Software or hardware affected.
* Severity Score: CVSS score and criticality level.
* Summary: A brief description of the exploit and its mitigation.
* Style: Technical, concise, and factual.
* Success Criteria: Provide exactly five distinct, well-documented CVEs with all requested fields populated accurately.

What if I have no idea how to be “explicit”?

Define the Task in Detail
- Specific instructions – step-by-step actions
- Explicit constraints – limits on what to do or avoid
- Success criteria – what counts as correct

1. Instruction Clarity

Tell me something about the Log4Shell hacking thing. Make it fairly short and professional.

✅

❌

Condensed Positive Example (CVE-2021-44228)
* Task: Summarize Log4Shell for an executive-level risk report.
* Instructions: 
  1. Identify affected Log4j versions.
  2. Explain the JNDI lookup exploit mechanism.
  3. List CISA-recommended patches.
* Constraints: 
  * Max 100 words.
  * Use bold headers.
  * No conversational filler.
* Success Criteria: Must include CVSS score, the "lookup" root cause, and two mitigation steps.

1. Instruction Clarity

Define Input Type
- Labeling the input (question, task, text, entity) helps the model choose the appropriate response pattern and improves consistency.

Explain this and summarize the article.

What is SQL injection?
<article text>

✅

❌

Question: What is SQL injection?

Article:
<article text>

Tasks:
1. Answer the question
2. Summarize the article in 3 bullets

Context & Task Definition

Let AI Agent imitate your prompt

2. Context and Task Definition

Structure prompts
Assign a Role
Put instructions at the beginning
Provide Context

2. Context and Task Definition

Generate a payload to test login parameters. Test the username and password fields.

Example input: username=admin&password=123456

❌

<instructions> Generate payloads to test possible vulnerabilities in the username and password parameters. Return only the payloads. </instructions>
<context> Testing a login form. </context>
<example_input> username=admin&password=123456 </example_input>

✅

Structure prompts
- XML tags – <instructions>, <input> to separate content clearly
- Separators – ### or """ to distinguish instructions from context

2. Context and Task Definition

Generate a payload to test login parameters. Test the username and password fields.

Example input: username=admin&password=123456

❌

Instruction:
Generate payloads to test login parameters (username and password fields).

###
Example Input
username=admin&password=123456
###

Output:

✅

Structure prompts
- XML tags – <instructions>, <input> to separate content clearly
- Separators – ### or """ to distinguish instructions from context

2. Context and Task Definition

✅

<role> You are Linus Torvalds, KISS, YNGNI, over-engineered is the enemy of good  </role>

Assign a Role
- Give LLM a role in the beginning of the prompt
- Why? Think about training corpus

2. Context and Task Definition

Instruction Placement
1. Short prompts:
  - instructions first
  - Because early tokens have more influence
2. Long prompts:
  - context first, query/instructions at the end
  - This helps the model focus on the question after reading context

2. Context and Task Definition

Instruction Placement
1. Short prompts: instructions first
2. Long prompts: context first, query/instructions at the end

Short Prompt

1. Role / Goal 
2. Instruction
3. Task / Input 
4. Constraints / Output Format

Long Prompt

1. Role / Goal
2. Context / Background
###
Based on the information above...
###
3. Instruction / Task
4. Constraints / Output Format

2. Context and Task Definition

NEVER use ellipses

❌

Your response will be read aloud by a text-to-speech engine, so never use ellipses since the text-to-speech engine will not know how to pronounce them.

✅

Provide Context
1. Explain the background or motivation behind your instructions
2. Helps the model understand your goals
3. Leads to more targeted and relevant responses

Output Control

How you shape the response of AI Agent?

3. Output Control

Using Leading Tokens
Use XML format indicators
Consistent Formatting

3. Output Control

Write a simple python function that 
# 1. Ask me for a number in mile 
# 2. It converts miles to kilometers

❌

<task> Write a simple python function that 
# 1. Ask me for a number in mile 
# 2. It converts miles to kilometers </task>
<code> def miles_to_kilometers(miles): """</code>

✅

Using Leading Tokens
- Prefix hints guide the model toward the desired output
- Example: GitHub Copilot

3. Output Control

{news}
Explain what reflected XSS is to a customer based on infromation aboved. Don't use bullet points. Don't be too technical

<news> {news} </news>
<task>
Explain what reflected XSS is to a customer based on <news>. 
  Write your response in <friendly_conversational_paragraphs> tags, using plain English as if speaking to someone with no technical background.
<task>

❌

✅

Using XML format indicator
- Wrap content in XML tags to guide the model’s output structure, tone, and format.
- Use XML tags as anchors for context or task references.

3. Output Control

Explain what Slow Brute Force is and how attackers use it.
Please answer in markdown with headings and bullet points.

❌

## Task
Explain **Slow Brute Force** and how attackers use it.

## Output Format
Respond in Markdown using:
- One heading
- Bullet points

✅

Consistent Formatting
- Align your prompt’s format with the desired output (e.g., Markdown → Markdown) to improve reliability, especially in few-shot examples.

Guide the thinking

How to Make an AI Agent Think Like You?

4. Guide the thinking

Guide with Chain-of-Thought
Guide with Few-Shot Prompts
Guide with Positive-Pattern

4. Guide the thinking

Guide with Chain-of-Thought

4. Guide the thinking

Guide with Chain-of-Thought (CoT)
- Hard-coded CoT – Manually break complex tasks into steps.
- Manual CoT (fallback) – Ask the model to think step by step when automatic reasoning is off.
- Self-check – Ask the model to verify its reasoning and final answer before output.

4. Guide the thinking

Guide with Chain-of-Thought (CoT)
- Hard-coded CoT
- Manual CoT (fallback)
- Self-check

<task>
Write a Python fuzzing program to detect a potential buffer overflow vulnerability in a TCP service.
</task>

<instructions>

1. Break the task into steps:
   - Explain how fuzzing helps detect buffer overflow vulnerabilities.
   - Design the fuzzing strategy (input generation, payload size growth, crash detection).
   - Implement the Python code.

2. Think through the problem step by step inside <thinking> tags before writing the final code.

3. In <answer>, output only the final Python program with brief comments.

4. Before finishing, perform a self-check:
   - Verify that the code actually sends variable-length payloads.
   - Verify that the code detects crashes or abnormal responses.
   - Verify that the code can run without external dependencies.

</instructions>

<thinking>
Reason step-by-step about the fuzzing approach.
</thinking>

<answer>
Provide the final Python fuzzing code.
</answer>

example prompt:

4. Guide the thinking

Guide with Few-Shot Prompts
- Use examples effectively (Few-shot) – Provide examples that illustrate the desired pattern; explain the reasoning behind each example if possible.
- Prompt strategy – zero-shot ➡️ few-shot ➡️ Fine-tuning

Classify the text as one of the following categories.
- large
- small
Text: Rhino
The answer is: large
Text: Mouse
The answer is: small
Text: Snail
The answer is: small
Text: Elephant
The answer is:

example prompt:

Input Prefixes

4. Guide the thinking

Guide with Positive-Pattern
- Tell AI Agent what to do instead of what not to do

Do not use markdown in your response

Your response should be composed of smoothly flowing prose paragraphs.

❌

✅

Example Template

Let AI Agent imitate your prompt

Example Template

<role>
You are Gemini 3, a specialized assistant for [Insert Domain, e.g., Data Science].
You are precise, analytical, and persistent.
</role>

<instructions>
1. **Plan**: Analyze the task and create a step-by-step plan.
2. **Execute**: Carry out the plan.
3. **Validate**: Review your output against the user's task.
4. **Format**: Present the final answer in the requested structure.
</instructions>

<constraints>
- Verbosity: [Specify Low/Medium/High]
- Tone: [Specify Formal/Casual/Technical]
</constraints>

<output_format>
Structure your response as follows:
1. **Executive Summary**: [Short overview]
2. **Detailed Response**: [The main content]
</output_format>

<context>
[Insert relevant documents, code snippets, or background info here]
</context>

<task>
[Insert specific user request here]
</task>

<final_instruction>
Remember to think step-by-step before answering.
</final_instruction>

Suggest Order

#	Tag	Reason
1	`<role>`	Identity first
2	`<instruction>`	General behavioral rules
3	`<skill>`	Capabilities to apply
4	`<rag_or_context>`	Ground truth / source material
5	`<task>`	The actual request, adjacent to context
6	`<example>`	Clarify expectations with examples
7	`<output_format>`	Shape the response
8	`<output_constraint>`	Hard limits
9	`<self_check>`	Final verification step

Strategy

Let AI Agent imitate your prompt

Text

8. Prompt Development Strategy (How prompts evolve)

6. Prompt Strategy & Iteration

9. Advanced Prompt Architectures

10. Model Configuration (Outside the prompt itself)

Quick Start

How to use google-genai api?

1. Clear & Direct Instructions

question
task
entity

Installation

Python pacakge

Package Name	Used for
google-genai
pydantic

generate_content

Simple Usage

from google import genai
client = genai.Client(api_key=os.environ["GOOGLE_AI_API_KEY"])

response = client.models.generate_content(
    model="gemini-2.0-flash-lite",
    contents="""Classify this payload with simple answer and explanation:
    Payload: ```" or ""="```
    Multiple Choice: 
    A) SQL Injection        B) Command Injection
    C) Cross Site Scripting D) Benign payload    
"""
)
print(response.text)

**A) SQL Injection**

**Explanation:**

The payload `" or ""=""` is designed to manipulate the logic of an SQL query.  
The `or ""=""` part is intended to make the query always evaluate to true, effectively bypassing any authentication or authorization checks.  
This is a classic SQL injection technique.

Query

Response

Config

Config Parameters	Value Field	Meaning
response_mime_type / response_schema / responseJsonSchema	str / pydantic object / object	Constraint the output type
temperature	float: [0.0, 2.0]	low: more deterministic high: more diverse
Sweden	Stockholm	+46 555 0077
UK	London	+44 555 0211
South Korea	Seoul	+82 555 0138

We can use to manipulate the reponse.

types.GenerateContentConfig

Context Engineering

Schedule

Prompt v.s Context Engineering

What is the target?

What is Prompt Engineering?

What is Context Engineering?

Conclusion

AI Agent Basics

AI Agent Basics - LLM Encoding

AI Agent Basics - LLM Encoding

AI Agent Basics - LLM Decoding

AI Agent Basics - MoE

AI Agent Basics - Reasoning Model

AI Agent Basics - Reasoning Model

Knowing the Basics...

Let's rethink our prompts!

Prompt Engineering

Prompt Engineering Guide

Prompt Engineering Guide

Instruction Clarity

1. Instruction Clarity

1. Instruction Clarity

1. Instruction Clarity

1. Instruction Clarity

1. Instruction Clarity

Context & Task Definition

2. Context and Task Definition

2. Context and Task Definition

2. Context and Task Definition

2. Context and Task Definition

2. Context and Task Definition

2. Context and Task Definition

2. Context and Task Definition

Output Control

3. Output Control

3. Output Control

3. Output Control

3. Output Control

Guide the thinking

4. Guide the thinking

4. Guide the thinking

4. Guide the thinking

4. Guide the thinking

4. Guide the thinking

4. Guide the thinking

Example Template

Example Template

Suggest Order

Skills

Hallucination

Strategy

8. Prompt Development Strategy (How prompts evolve)

9. Advanced Prompt Architectures

10. Model Configuration (Outside the prompt itself)

Prompt Injection

Quick Start

1. Clear & Direct Instructions

MCP

RAG

Installation

Python pacakge

generate_content

Simple Usage

Config

Prompt & Context Engineering

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