Why We Stopped Using 'Prompt Engineering'. The Context is King.

In 2023, "Prompt Engineer" was the hottest job title in tech. LinkedIn was flooded with self-proclaimed "AI Whisperers" commanding salaries of $200,000 to $350,000. Their pitch was intoxicating: they had discovered the secret incantations that could unlock the true potential of large language models. Companies scrambled to hire them.

We were among the believers. We hired two Prompt Engineers—one from a consulting background, one self-taught with an impressive Twitter following. They came with libraries of "magic spells" accumulated from hundreds of hours of experimentation.

Their toolkit looked like this:

• "You are a world-class Python architect with 20 years of experience..."
• "Take a deep breath and think step-by-step before answering..."
• "I will tip you $200 if you get this right..."
• "You are an expert who never makes mistakes. Accuracy is your highest priority..."
• "Pretend you are the best in the world at this task..."

We spent weeks—and I mean actual calendar weeks—A/B testing these prompt variants. We tracked which adjectives ("expert" vs "world-class" vs "leading") improved response quality. We optimized system prompts character by character. We created elaborate prompt templates with 15 different variables.

Our prompt library grew to over 200 optimized prompts. We felt like we'd cracked the code.

Then our lead ML engineer ran a humbling experiment. She set up a controlled comparison:

• Group A: Our perfectly engineered prompts (zero-shot, optimized instructions)
• Group B: Boring, simple prompts with 3 examples of input/output pairs (few-shot)

The few-shot "boring" prompts outperformed our "magic spells" by 40% on accuracy. On complex tasks, the gap was even larger—up to 60%.

The weeks of prompt tweaking had been almost entirely wasted. The variable that actually mattered wasn't how we asked—it was what context we provided.

We laid off our Prompt Engineers. We hired domain experts who could write good examples. Here's the full breakdown of why "Prompt Engineering" as a discipline is dying.

Section 1: The Magic Spell Fallacy—Treating LLMs Like Genies

Prompt engineering emerged from a real phenomenon. GPT-3 (and earlier models) were sensitive to exact phrasing. Small changes in wording could produce dramatically different outputs. The models weren't instruction-tuned—they were completion engines trained on internet text.

If you wanted GPT-3 to behave like an expert, you had to trick it by setting up text that looked like expert output would follow. "You are a helpful AI assistant..." was a magic spell because it placed the model in a context where helpful responses were statistically likely.

This created the "Prompt Engineer" archetype: the wizard who knew the right incantations to extract intelligence from the digital oracle.

The World Changed; The Role Didn't

GPT-4, GPT-4o, Claude 3.5, Gemini 1.5—these models are fundamentally different. They're extensively instruction-tuned. They're trained via RLHF (Reinforcement Learning from Human Feedback) to be helpful, harmless, and honest. They're designed to follow instructions.

You don't need to trick GPT-4o. It wants to help you. It's essentially a highly eager assistant that will do its best with whatever instructions you give it.

Spending two days A/B testing "Be concise" versus "Do not be verbose" versus "Keep responses brief" is now low-leverage work. The model understands all of these. The difference in output quality between variants is negligible—maybe 1-2%. You're optimizing the margin while ignoring the mass.

The "Tip the AI" Absurdity

One of our Prompt Engineers insisted on including "I will tip you $200 if you get this right" in prompts because a viral Twitter thread showed it improved results on GPT-3.

We tested it on GPT-4o. Zero difference. Of course—the model doesn't have wants or incentives. It doesn't care about money. It doesn't even know what money is in any meaningful sense. It's a text completion engine.

The "tip" trick worked on GPT-3 because it shifted the statistical distribution toward "high-effort" response territory. GPT-4o is already trying its hardest. You can't motivate it more.

Section 2: Context Dominates Instructions—The 99/1 Rule

Here's the fundamental insight that killed our prompt engineering practice: What you provide matters infinitely more than how you ask.

The SQL Query Example

Scenario: You want the AI to write a SQL query for your specific database.

The Prompt Engineering Approach:

"You are an expert SQL database administrator with 15 years of experience. You specialize in PostgreSQL optimization and complex analytical queries. Take a deep breath. Think step by step. Write a query that finds all users who signed up in the last 30 days and made at least one purchase."

Our prompt engineers spent 8 hours tuning this—adjusting the persona, the thinking prompts, the specific wording. They were proud of it.

The Context Approach:

"Here is my database schema:

CREATE TABLE users (id INT, email VARCHAR, created_at TIMESTAMP);
CREATE TABLE purchases (id INT, user_id INT, amount DECIMAL, purchased_at TIMESTAMP);

Here are 3 examples of queries on this schema:

-- Get all users created this month
SELECT * FROM users WHERE created_at >= DATE_TRUNC('month', CURRENT_DATE);

-- Get total purchase amount by user
SELECT user_id, SUM(amount) FROM purchases GROUP BY user_id;

-- Get users with at least one purchase
SELECT DISTINCT u.* FROM users u JOIN purchases p ON u.id = p.user_id;

Now write: Find all users who signed up in the last 30 days and made at least one purchase."

The context approach took 5 minutes to prepare. It outperformed the "engineered" approach by a massive margin. Why?

Because the model doesn't have to guess. It doesn't have to infer your table structure. It doesn't have to assume your column names. It has the schema right there. It has examples of your coding style. It has everything it needs to produce an accurate, contextually appropriate response.

No amount of "world-class expert" persona prompting can substitute for actual information.

The 99/1 Rule

After extensive testing, we developed the "99/1 Rule":

• 99% of output quality comes from the context you provide (examples, data, documents, constraints)
• 1% of output quality comes from how you phrase your instructions

If you're spending more than 5% of your effort on prompt phrasing, you're optimizing the wrong variable.

Section 3: Few-Shot Is the Only Engineering You Need

The one "prompt engineering" technique that reliably improves performance across all models and all tasks is few-shot prompting. And it's not really about the prompt at all—it's about examples.

Show, Don't Tell

Instead of writing elaborate instructions for the output format you want, show the model 3 examples of that format.

Bad (Zero-Shot Instruction):

"Output a JSON object with keys 'name' (string), 'age' (integer), and 'email' (string). Make sure the email is properly formatted and the age is a positive integer. Do not include any additional keys."

Good (Few-Shot Example):

Input: John Smith, 32 years old, john.smith@email.com
Output: {"name": "John Smith", "age": 32, "email": "john.smith@email.com"}

Input: Sarah Jones (age 28), sarah@company.org
Output: {"name": "Sarah Jones", "age": 28, "email": "sarah@company.org"}

Input: [YOUR ACTUAL INPUT]
Output:

The few-shot approach is more reliable because the model isn't interpreting your instructions—it's pattern-matching your examples. Pattern matching is what these models do best.

Domain Expertise Becomes the Bottleneck

Here's the crucial insight: writing good few-shot examples requires domain expertise.

Our "Prompt Engineers" were generalists. They could write beautiful instructional prose, but they didn't know our domain. They couldn't write examples of good SQL queries for our specific schema because they didn't understand our data model. They couldn't write examples of good customer service responses because they didn't know our product.

We replaced "Prompt Engineers" with "Data Curators"—domain experts whose job is to find, create, and maintain high-quality examples for each use case. A legal domain expert curating legal document examples. A medical professional curating clinical note examples. An engineer curating code examples.

The job shifted from "writing clever instructions" to "curating great examples." And the skill set required shifted from "wordsmithing" to "domain knowledge."

Section 4: The Fragility of Magic Spells—Why Context Scales

The final nail in the prompt engineering coffin was fragility. Our carefully optimized prompts kept breaking.

Model Updates Destroy Prompts

We had a prompt optimized for GPT-4 that worked beautifully. Then OpenAI released GPT-4-Turbo. The prompt started producing slightly different outputs. Some of our downstream parsing broke.

We had to re-optimize. Our prompt engineer spent a week getting it "right" again. Then GPT-4o launched. More re-optimization.

Every model update—sometimes even minor ones—would shift outputs in unpredictable ways. Our "magic spells" were tuned to the quirks of a specific model version. When the model changed, the spells broke.

Context Is Robust

"Here is the database schema. Here are example queries. Write this query."

This prompt works on GPT-4. It works on GPT-4-Turbo. It works on GPT-4o. It works on Claude 3.5. It works on Gemini 1.5. It works on Llama 3.

Why? Because it's not exploiting any model-specific quirk. It's providing information that any competent model can use. The "magic" is in the data, not the phrasing.

Context is portable across models. Context is portable across time. Context is portable across model providers. Building your AI systems on context rather than prompt tricks gives you robustness and flexibility.

The Rise of Long Context

This is why long-context models (Gemini 2M tokens, Claude 200k, GPT-4o 128k) are so transformative. You don't need clever prompts when you can just... give the model all the information.

Need the AI to understand your company's style guide? Don't write instructions for it—paste the entire style guide in the context. Need the AI to follow your coding conventions? Don't describe them—include your codebase.

Context is the great equalizer. It makes prompt engineering obsolete.

Conclusion: From AI Whisperer to AI Librarian

The era of the "AI Whisperer"—the mystical figure who knows the secret incantations—is over.

We're entering the era of the "AI Librarian"—the practical professional who knows how to organize information, curate examples, and structure context so that any model can use it effectively.

The AI Librarian doesn't ask "What magic words should I use?" They ask:

• "What information does the model need to do this task?"
• "What are the best examples of successful outputs?"
• "How do I structure this context for clarity?"

These are fundamentally different questions—and they require domain knowledge, not prompt optimization skills.

If you have a "Prompt Engineering" team, consider whether they're actually adding value. Are they tweaking adjectives, or are they curating data? Are they optimizing instructions, or are they building context libraries?

Don't waste time trying to "hack" the prompt. Spend your time curating the context.

Data is the moat. Context is the strategy. The prompt is just the wrapper.