Learn / AI Literacy / Context and Memory

Context and Memory

10 min understand 4 sections
Step 1 of 4

WHY WHY This Matters

AI models don't have persistent memory the way humans do. Every interaction starts fresh—unless you deliberately provide context. Understanding how AI "remembers" is crucial because:

  • Quality depends on context: The same question with different context produces dramatically different answers
  • Consistency requires structure: Without explicit context management, AI responses drift and contradict
  • Cost scales with context: More context = more tokens = higher costs

Mastering context management is what separates effective AI operators from frustrated users.

Step 2 of 4

WHAT WHAT You Need to Know

The Stateless Reality

What this means in practice:

  • The AI doesn't "know" you between sessions
  • Personality and preferences must be re-established each time
  • Long conversations eventually hit context limits

System Prompts: Setting the Stage

Effective system prompt structure:

You are [ROLE] with expertise in [DOMAIN].

Your responsibilities:
- [Primary function]
- [Secondary function]

When responding:
- [Style guideline]
- [Format requirement]
- [Constraint]

Context about the user/situation:
[Relevant background that applies to all interactions]

Conversation History Management

As conversations grow, you face the context window squeeze:

Conversation Length Challenge Strategy
Short (< 2K tokens) None Include full history
Medium (2K-10K tokens) Cost Summarize older exchanges
Long (10K+ tokens) Quality degradation Rolling summary + recent messages
Very Long (near limit) Truncation required Strategic pruning

The sliding window approach:

  1. Keep the system prompt (always)
  2. Keep a running summary of key decisions/context
  3. Keep the most recent 3-5 exchanges in full
  4. Summarize or drop older messages

Context Injection Patterns

Example transformation:

❌ Without context injection:

"Write a response to this customer complaint"

✅ With context injection:

"Write a response to this customer complaint.

Customer context:

  • Premium member since 2019
  • 3 previous support tickets (all resolved satisfactorily)
  • Current issue: Delayed shipment (5 days late)
  • Company policy: Offer 20% discount for delays > 3 days

Complaint: [customer message]"

Key Concepts

Key Concept

stateless

AI models are fundamentally stateless—they don't remember previous conversations unless you explicitly include that history in your current prompt.

When you chat with ChatGPT, the interface manages this for you by sending your entire conversation history with each message. But behind the scenes, every API call is independent.

Key Concept

system prompt

A system prompt is special instruction text that shapes how the AI behaves throughout a conversation. It's like giving an actor their character brief before a scene.

System prompts typically include:

  • Role definition: "You are a financial analyst specializing in retail..."
  • Behavioral constraints: "Always cite sources. Never make up statistics."
  • Output format: "Respond in bullet points. Keep responses under 200 words."
  • Domain context: Background information relevant to all interactions
Key Concept

context injection

Context injection is the practice of inserting relevant information into prompts so the AI can make informed decisions without having to "know" everything.

Types of injected context:

  • User data: "The customer's account was created 3 years ago and they've made 47 purchases"
  • Document excerpts: "Based on section 4.2 of the policy: [relevant text]"
  • Current state: "Current inventory: 150 units. Reorder threshold: 100 units."
  • Constraints: "Budget remaining: $5,000. Timeline: 2 weeks."
Step 3 of 4

HOW HOW to Apply This

Exercise: Design a Context Strategy

Context Management Patterns

Pattern Use Case Implementation
Full History Short, high-value conversations Send everything
Rolling Summary Long-running assistants Summarize after each exchange
Retrieval Augmented Knowledge-heavy domains Fetch relevant docs per query
Hybrid Complex applications Summary + retrieved + recent

Common Mistakes to Avoid

Mistake Why It's Bad Better Approach
Assuming AI remembers Inconsistent results Always provide necessary context
Overloading system prompt Wasted tokens, confused behavior Keep it focused, inject dynamic data
Ignoring context limits Truncated/degraded responses Monitor and manage proactively
No summarization strategy Expensive, eventually breaks Plan for long conversations

Self-Check

Practice Exercises

You're building an AI assistant for sales representatives to help them prepare for customer calls.

Design the context structure:

  1. What goes in the system prompt? (persistent context)
  2. What gets injected per-call? (dynamic context)
  3. How do you handle multi-call history with the same customer?
  4. What's your strategy when approaching context limits?

Consider:

  • CRM data availability
  • Call notes from previous interactions
  • Product/pricing information
  • Competitive intelligence
Step 4 of 4

GENERIC Up Next

In Module 1.3: Providers and Models, you'll learn how to navigate the AI landscape—comparing capabilities, costs, and use cases across major providers.

Module Complete!

You've reached the end of this module. Review the checklist below to ensure you've understood the key concepts.

Progress Checklist

0/5
0% Complete
0/4 Sections
0/3 Concepts
0/1 Exercises