AudienceAdapter

Category: Communication | Module: mycontext.templates.free.communication

Adapts content for a different audience. Analyzes the target audience's expertise level, interests, and communication preferences — then rewrites the message with appropriate language, relevant examples, adjusted tone, and optimized structure. Preserves accuracy while maximizing resonance.

When to Use

• Translating technical content for executives
• Adapting developer documentation for end-users
• Preparing the same announcement for different teams
• Sales pitches customized by industry or role
• Research findings summarized for different audiences
• Internal memos adapted for external publishing

Quick Start

from mycontext.templates.free.communication import AudienceAdapter

adapter = AudienceAdapter()

ctx = adapter.build_context(
    message="Our new transformer-based NLP pipeline achieves 94% F1 score on the benchmark with 2.3B parameters",
    current_audience="ML engineers",
    target_audience="C-suite executives",
)
result = ctx.execute(provider="openai")
print(result.response)

Methods

build_context(message, current_audience="general", target_audience="specific group", context=None)

Parameters:

Parameter	Type	Default	Description
message	str	""	The content to adapt
current_audience	str	"general"	Who the message was written for
target_audience	str	"specific group"	Who you want to reach
context	str | None	None	Purpose or additional context

execute(provider, message, current_audience, target_audience, context=None, **kwargs)

result = adapter.execute(
    provider="openai",
    message=your_technical_message,
    current_audience="engineering team",
    target_audience="board of directors",
)

Adaptation Dimensions

The pattern adjusts content across six dimensions simultaneously:

Dimension	What changes
Language	Technical level, jargon replacement, complexity
Framing	What's emphasized based on audience priorities
Examples & Analogies	Swapped for ones relevant to the target audience's domain
Tone	Formal/casual, authoritative/collaborative, detail/big-picture
Structure	Reorganized for how this audience consumes information
Length	Condensed or expanded based on attention span

Common Adaptations

Technical → Executive

result = adapter.execute(
    provider="openai",
    message="We've implemented a Redis-based distributed cache with LRU eviction, reducing p99 latency from 850ms to 120ms",
    current_audience="DevOps engineers",
    target_audience="VP of Product",
)

Adaptation effects:

• Redis/LRU → "smart memory system"
• p99 latency → "slowest 1% of requests"
• Framing shifts from technical spec → business value (8x faster = better user experience)

Developer Docs → End User Docs

result = adapter.execute(
    provider="anthropic",
    message="Configure OAuth2 PKCE flow with token refresh via the /auth/refresh endpoint with Bearer authentication",
    current_audience="developers",
    target_audience="non-technical business users",
)

Research → General Public

result = adapter.execute(
    provider="gemini",
    message="Meta-analysis of 23 RCTs (n=4,521) shows MBSR intervention has statistically significant effect on GAD-7 scores (p<0.001, d=0.48)",
    current_audience="clinical researchers",
    target_audience="people managing anxiety",
)

12-Section Output

1. Target Audience Analysis — Who they are, what they care about, communication preferences
2. Language Adaptation — Technical level adjustment, jargon translation table
3. Core Message Extraction — Essential points to preserve, what to emphasize/minimize
4. Framing for Audience — Value proposition reframed through their lens
5. Example & Analogy Adaptation — New examples relevant to target domain
6. Tone & Style Adjustment — Formality, energy, confidence levels
7. Structure Optimization — Opening, organization, flow, closing
8. Adapted Message — Fully rewritten version
9. Key Adaptations Made — Summary of every change and why
10. Potential Questions — What this audience will likely ask (with prepared answers)
11. Delivery Recommendations — Medium, timing, setting, support materials
12. Success Indicators — How to know the adaptation worked

Multiple Audiences

Adapt the same message for multiple audiences:

message = "We're rebuilding our infrastructure on Kubernetes with a 3-month migration timeline"

audiences = {
    "engineering": "DevOps and platform engineers",
    "sales": "Account executives closing enterprise deals",
    "investors": "Series B investors reviewing technical roadmap",
}

for key, audience in audiences.items():
    result = adapter.execute(
        provider="openai",
        message=message,
        current_audience="CTO",
        target_audience=audience,
    )
    print(f"\n--- For {key} ---")
    print(result.response)

API Reference

Method	Returns	Description
build_context(message, current_audience, target_audience, context)	Context	Assembled context
execute(provider, message, current_audience, target_audience, context, **kwargs)	ProviderResponse	Execute adaptation
generic_prompt(message, current_audience, target_audience, context_section)	str	Zero-cost prompt string