LLM

LLM

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Prompt Engineering

Scaffold a Spring Boot application

Generate a Spring Boot service with the following requirements:

1. Use Java 21
2. Use latest Spring Boot GA version
3. Package name: com.example.demo

using Spring Data Rest, PostgreSQL as database
with dependencies: Lombok
with base package: cq.project.reading_companion
with entities:
class Book { String name, String isbn10, String isbn13, Storage storage}
enum Storage { BOX, KINDLE, GOOGLE_PLAY_BOOKS }
class Position { String isbn13, int page, LocalDateTime when }

Java code:
Use the following Lombok annotations whenever applicable:
@Data, @Builder, @NoArgsConstructor, @AllArgsConstructor, @RequiredArgsConstructor
to replace getters, setters, constructors, Builder patterns
Use @EqualsAndHashCode for equals and hashCode methods
Use @Slf4j in all classes to enable logging with SLF4J

For DTO classes
1. Make all fields final
2. Add an all arguments constructor

For constructors or methods with more than 3 arguments, use a new line for every argument
Use var for local variable declaration

Testing:
Use AssertJ for assertions and matchers
Use Testcontainers for integration testing, don't use H2 or any embedded DB

Use @RequiredArgsConstructor for constructor injection for Spring beans,
Use Objects.toString to convert row values if String is needed

For JPA entities:
Use @JdbcType(PostgreSQLEnumJdbcType.class) for mapping PostgreSQL enums

Remove all comments in the source code

Framework for LLM use case evaluation

Type of customer needExampleML Implementation (Yes/No/Depends)Type of ML Implementation
Repetitive tasks where a customer needs the same output for the same inputAdd my email across various forms onlineNoCreating a rules-based system is more than sufficient to help you with your outputs
Repetitive tasks where a customer needs different outputs for the same inputThe customer is in “discovery mode” and expects a new experience when taking the same action:
- Generate a new artwork per click
- StumbleUpon style exploration
Yes- Image generation LLMs
- Recommendation algorithms (collaborative filtering)
Repetitive tasks where a customer needs the same/similar output for different inputs- Grading essays
- Generating themes from customer feedback
DependsIf simple: rules-based system works.
If complex combinations:
- Classifiers
- Topic modelling
Use LLMs for patternless or one-off cases
Repetitive tasks where a customer needs different outputs for different inputs- Answering customer support questions
- Search
YesToo many permutations for rules-based systems. Consider:
- LLMs with retrieval-augmented generation (RAG)
- Decision trees for products such as search
Non-repetitive tasks with different outputsReview of a hotel/restaurantYesPre-LLMs needed specialized models:
- Recurrent neural networks (RNNs)
- LSTMs
LLMs are a great fit for this type of scenario