Large Model Prompt Engineering: From Theory to Practice

1. Basic knowledge of Prompt technology (I) Analysis of core concepts In the application scenario of large language model (LLM), Prompt plays a key role as a "capability trigger". Different from the imperative interaction of traditional database queries, Prompt activates the distributed knowledge representation formed in the model pre-training stage through a semantic guidance mechanism, realizing a leapfrog transformation from a static knowledge base to a dynamic intelligent agent. (II) Basic framework system Modern Prompt engineering has formed a standardized design paradigm, which mainly includes five core elements:

1. Role definition (Role) limits the knowledge boundary through identity anchoring. In the medical scenario, "You are the attending physician of the cardiology department of a tertiary hospital" can make the diagnostic recommendations comply with clinical guidelines. 2. Task instructions (Task) need to use verb-driven expressions, such as "Please compare and analyze" triggers deeper logical reasoning than "compare". 3. Context construction (Context) supports multimodal input integration. Typical cases include synchronizing multi-dimensional information such as user historical orders, product reviews, and logistics status in e-commerce customer service. 4. Format specification (Format) Structured output can improve downstream processing efficiency. JSON-LD format output increases the efficiency of knowledge graph construction by 68% (Google Research data). 5. Evaluation criteria (Evaluation) The introduction of an automatic scoring mechanism can achieve iterative optimization, such as embedding compliance verification indicators in legal document generation tasks.

2. Efficient Prompt Engineering Strategy (I) Instruction Optimization Methodology 1. Semantic Isolation Technology Use triple backticks (```) to establish input isolation zones to avoid instruction contamination. Tests show that the error rate of legal document generation is as high as 37% when isolation characters are not used, but it drops to 9.2% after standardized use.

• Triple double quotes """ are often used to wrap longer text strings in code writing.

• Triple single quotes ''', similar in function to """, can be used in nested text

• Triple dash --- , separation

• Angle brackets <>, marking specific elements

• XML tags, use structured tags to distinguish different contents

DeepSeek official example:

2. Structured output protocol specifies the output format in the prompt word

prompt= "Please briefly analyze blockchain technology, including core ideas, technical features and application scenarios, and output in JSON format"
{  "answer" : {    "Core View" :  "The essence of blockchain technology is a distributed trust machine" ,    "Technical features" : [ "decentralization" ,  "immutability" ,  "smart contracts" ],    "Application scenarios" : [ "Financial settlement" ,  "Supply chain traceability" ,  "Digital identity" ]  }}

DeepSeek official example:

This format makes the information extraction accuracy reach 91.4% (Stanford NLP group test data). 3. Conditional verification mechanism

When facing complex tasks, if there are some assumptions that are not always true, we need to guide the model to check these assumptions first. If the conditions are not met, the model should clearly indicate and stop continuing the task to avoid invalid calculations and wrong results. At the same time, considering potential edge cases and setting corresponding processing rules for the model can effectively reduce unexpected errors and ensure the stability and reliability of the model output.

prompt= "Analyze the current balance sheet and issue a warning if the assets are lower than the liability red line"
if  total_assets < liability_threshold:    raise  ValueError ( "Assets are lower than the liability red line, financial risk warning!" )

(II) Deep Reasoning Enhancement Solution 1. Thinking Chain Guidance Paradigm

This mode can gradually improve the reasoning ability of large models, which is very suitable for processing some complex tasks . For example:

• Analytical or logical reasoning tasks

• decision making

• Solve the problem (for example, a programmer finds a bug based on the error log)

To use this pattern, just add "Let's think about it step by step" at the end . For example :

User instruction: Optimize the company's marketing budget allocation. Let's think about the model response step by step: Step 1: Analyze historical delivery data... Step 2: Evaluate the ROI of each channel... Step 3: Develop a dynamic adjustment strategy... Final solution: [Detailed budget allocation table]

2. Few-shot learning strategy

Providing a small number of successful examples when asking the model to run can significantly improve the model's performance.

Example 1: Question: How to calculate the area of ​​a triangle? Answer: Area = (base × height)/2 Example 2: Question: Find the circumference of a circle? Answer: Perimeter = 2πr New question: What is the formula for the area of ​​a trapezoid?

Experiments show that this method can improve the accuracy from 62% to 89% when facing complex problems. III. Strategies for dealing with model limitations (I) Governance of hallucinations In order to effectively deal with hallucinations, the model can be required to give valid references when outputting and trace the source based on the references. This method facilitates the verification and review of output results.

(II) Task complexity response 1. Dynamic context management uses sliding window technology to process ultra-long texts. GPT-4 supports 250,000 token context windows, and the accuracy of financial report analysis is improved by 37%. 2. Multi-stage reasoning framework

Phase 1: Data cleaning and feature extraction Phase 2: Pattern recognition and association analysis Phase 3: Decision tree construction and verification

IV. Industry Application Practice Cases (I) Intelligent Customer Service System

Role: Senior e-commerce customer service specialist Task: Handle customer complaints about delayed delivery Context: User order number #123456, product category electronic products, current inventory status out of stock Format requirements: Step-by-step solution + soothing words

Sample output:

1. Confirm order status: Your order will be delayed by 7-10 days due to chip supply shortage. 2. Compensation plan: Give a coupon for accessories worth 100 yuan. 3. Emergency measures: Provide SF Express expedited delivery options (+20 yuan). 4. Emotional comfort: We are deeply sorry and have upgraded the order priority.

2. Financial risk control modeling

Role: Financial Risk Manager (FRM) Task: Build a credit assessment model for small and micro enterprises Data range: Financial reports, tax records, and supply chain data for the past three years Evaluation indicators: KS value > 0.4, AUC > 0.85 Output format: Python scorecard code + feature importance chart

Generate code snippet:

from sklearn.linear_model import LogisticRegressionimport pandas as pd# Feature Engineeringdata = pd.read_csv('finance_data.csv')data['debt_ratio'] = data['total_liability'] / data['total_assets']# Model Trainingmodel = LogisticRegression(class_weight='balanced')model.fit(X_train, y_train)# Evaluation Reportprint("KS value:", metrics.roc_auc_score(y_test, model.predict_proba(X_test)[:,1]))