What LLM technique is right for you?
Should you use RAG? Agents? Fine tuning? Or stick to system prompts?
Wuth a growing suite of techniques available to product builders—from system and user prompts to fine-tuning, Retrieval-Augmented Generation (RAG), tools, and agents—it can be confusing to decide which approach to adopt for your use case — and the most performant option isn’t always the most expensive one! Each method comes with its own trade-offs, costs, and expertise requirements. Lets break down these techniques, understand when to use each, and whether to combine them for an effective outcome.
TL;DR: Here’s a handy decision matrix:
The Foundations: System and User Prompts
The best place to get started, and often the most useful, is the simple user prompt / system prompt setup. This is pretty much just using prompts to do your bidding. When you write a prompt, split it by a system behavior definition and a user input, and that’s it.
What They Are: System prompts define the behavior of the LLM (e.g., “You are a helpful assistant”), while user prompts encapsulate the input provided by the end-user. Together, they form the simplest way to interact with an LLM.
When to Use:
Rapid Prototyping and quick first launches: Perfect for proving immediate value from LLMs, this approach is especially useful when the LLM’s world knowledge is sufficient to interact with users. Some additional context can be added into system prompts, but it’s usually limited.
Limited Complexity: Best used when user needs are straightforward (e.g., answering FAQs or generating content).
Minimal Budget: Ideal for use cases where cost is a major constraint, as prompts alone require no additional infrastructure or model modifications.
Use Case Examples:
Travel: A chatbot answering common travel queries like “What’s the best time to visit Paris?” or “Can I carry a power bank on a flight?” - you would typically rely on the LLM’s world knowledge for this.
Ecommerce: Generating product descriptions or providing instant answers to “What are the dimensions of this sofa?” - you would typically pass on the product context (the catalog page) along with the user prompt.
Customer Service: Addressing FAQs such as “What is your return policy?” or “How can I track my order?” - you would typically pass topic specific customer support workflows along with the user prompt.
Business Outlook: This approach offers a quick and cost-effective way to enhance customer experience without extensive development or infrastructure investment. However, it lacks depth for handling complex or highly personalized interactions.
Advantages:
Low Cost: No need for additional computational resources.
Simplicity: Easy to set up and iterate on.
Flexibility: Quickly adaptable to new tasks.
Limitations:
No Context Retention: Each prompt is treated as a standalone interaction. If you want to have conversation continuity, you need to pass the previous user and LLM messages along with the user prompt.
Lack of Customization: Limited ability to tailor responses for domain-specific needs. Since all info is contained within the prompt, it’s difficult to answer from a large knowledge base, or build recommendations based on inventory etc.
Cost: Lowest among all methods; typically only API call costs.
Expertise Needed: Basic understanding of prompt design.
Retrieval-Augmented Generation (RAG)
What It Is: RAG integrates external data sources into LLM responses by retrieving relevant documents or data and appending it to the prompt. For example, user manuals, support manuals, and even supply data can be used alongside the LLM. The basic premise is, given a user prompt, search through a dataset and retrieve the most relevant user context. Append it to the prompt, and pass this to the LLM to respond.
When to Use:
Dynamic Data Needs: When the LLM must access real-time or frequently updated information (e.g., current news, weather updates).
Domain-Specific Knowledge: For proprietary or highly specific datasets that the model wasn’t trained on, or if the corpus of information is exceptionally large.
Regulatory Compliance: When responses need to be based on verified or auditable information.
Use Case Examples:
Travel: Providing real-time flight status updates or suggesting activities based on current weather conditions.
Ecommerce: Recommending products based on a user’s purchase history or inventory data.
Customer Service: Answering questions like “What are the latest promotions?” or “Is my order eligible for free shipping?”
Business Outlook: RAG enables businesses to bridge the gap between static LLM knowledge and dynamic, real-world data. It’s particularly useful for enhancing personalization and ensuring responses are accurate and up-to-date, making it a strong choice for competitive differentiation.
Advantages:
Enhanced Relevance: Incorporates the latest or domain-specific data.
Scalability: Handles large datasets effectively.
Limitations:
Latency: Retrieval can introduce delays in response times by adding one more step before generating a response.
Complexity: Requires a retrieval system (e.g., Pinecone, Weaviate) and integration with the LLM.
Incomplete Data Issues: If the retrieval system fails to fetch relevant data, the LLM’s response might lack accuracy.
Cost: Medium; involves additional infrastructure for indexing and retrieval.
Expertise Needed: Moderate; knowledge of retrieval systems, embeddings, and prompt tuning.
Fine-Tuning
What It Is: Fine-tuning involves training an LLM on a domain-specific dataset to specialize it for particular tasks or industries.
When to Use:
Highly Specialized Tasks: When the LLM needs deep expertise in a narrow domain (e.g., legal, medical, or scientific contexts), or when the outputs need to be structured and predictable (eg JSON formatted outputs).
Brand Consistency: To ensure outputs align with a specific tone or style.
Improving Base Model Limitations: When the base model struggles with task performance despite prompt optimization.
Use Case Examples:
Travel: Tailoring an assistant to provide expert advice on niche destinations or luxury travel planning.
Ecommerce: Ensuring product descriptions align with brand guidelines and customer preferences.
Customer Service: Creating a specialized bot for handling complex policy inquiries or multilingual support.
Business Outlook: Fine-tuning offers precision and long-term ROI for businesses with specialized needs, though the upfront investment can be significant. It’s ideal for companies that require high-quality, domain-specific outputs consistently.
Advantages:
Precision: Produces highly accurate and tailored responses.
Efficiency: Reduces the need for overly verbose prompts.
Limitations:
Upfront Cost: Fine-tuning requires significant computational resources.
Maintenance: Updated versions of the base model require re-tuning.
Cost: High; includes computational costs and potential licensing fees for access to base models.
Expertise Needed: High; requires expertise in machine learning, data preparation, and evaluation.
Tools and Function Calling
What They Are: Tools extend LLM capabilities by allowing them to invoke external functions, APIs, or workflows dynamically (Read more about tools and agents here). For example, an application that needs the LLM to do simple math operations may give it access to a calculator. The LLM, when it encounters a math problem, passes the relevant variables to the calculator, and then uses the output in its response.
When to Use:
Task Automation: For applications that require executing specific actions, like booking tickets or checking inventory.
Contextual Responses: When the LLM needs to gather information dynamically (e.g., fetching user details from a database).
Interactive Workflows: Ideal for use cases requiring multiple steps (e.g., assembling reports, running calculations).
Use Case Examples:
Travel: Booking flights or hotels based on user preferences and available options.
Ecommerce: Checking product availability and placing orders directly from a chatbot interface.
Customer Service: Initiating refunds, modifying orders, or providing account-specific details.
Business Outlook: The ability to automate actions increases operational efficiency and reduces friction in user interactions. While initial setup costs can be high, tools unlock significant value in automating repetitive or complex workflows.
Advantages:
Dynamic Capabilities: Extends the LLM’s usefulness beyond static text generation.
Modularity: New functions can be added without retraining the model.
Limitations:
Rigidity: Limited to predefined functions.
Development Overhead: Requires backend integration and robust validation.
Cost: Medium to High; depends on the number of API calls and backend requirements.
Expertise Needed: Moderate; familiarity with API integration and LLM prompt design.
Agents
What They Are: Agents are advanced LLMs that use dynamic reasoning to plan and execute multi-step tasks, leveraging multiple tools or APIs autonomously (read more about agents here).
When to Use:
Complex Problem Solving: When workflows involve multiple interdependent steps.
Adaptable Systems: For tasks that require reasoning and decision-making on-the-fly.
End-to-End Solutions: When the LLM needs to autonomously achieve goals with minimal human input.
Use Case Examples:
Travel: Creating personalized itineraries by combining weather forecasts, user preferences, and activity options.
Ecommerce: Assisting users with end-to-end purchase decisions, from product discovery to delivery tracking.
Customer Service: Handling escalations by dynamically pulling data, invoking tools, and proposing solutions autonomously.
Business Outlook: Agents offer transformative potential by automating complex tasks with minimal human oversight. However, output unpredictability and high operational costs mean they’re best suited for high-value or experimental use cases.
Advantages:
Autonomy: Reduces human oversight for repetitive or complex workflows.
Context Retention: Maintains continuity across multi-step interactions.
Limitations:
Cost: Agents may make multiple API calls, significantly increasing costs.
Unpredictability: Requires rigorous error handling to avoid unexpected behaviors.
Cost: Very High; due to complexity, API usage, and monitoring requirements.
Expertise Needed: High; includes prompt engineering, tool integration, and error handling.
Combining Techniques
What It Is: Using multiple techniques together—e.g., combining RAG with tools or fine-tuning with agents—to maximize capabilities.
When to Use:
Hybrid Needs: When no single technique fully addresses your requirements.
Scale and Specialization: To handle diverse user needs (e.g., personalized recommendations + real-time data retrieval).
Future-Proofing: For systems that need to adapt as LLM capabilities evolve.
Use Case Examples:
Travel: Combining RAG for real-time flight data with agents for personalized travel planning.
Ecommerce: Using fine-tuned models for tailored product descriptions alongside tools for inventory management.
Customer Service: Integrating RAG for policy retrieval with agents for handling multi-step issue resolution.
Business Outlook: Hybrid approaches deliver comprehensive solutions but come with increased complexity and costs. They’re best for organizations seeking to build robust, scalable, and future-proof systems.
Advantages:
Comprehensive Solutions: Addresses multiple challenges simultaneously.
Resilience: Reduces reliance on a single point of failure.
Limitations:
Complexity: Increases development and maintenance efforts.
Cost: Combines the costs of all involved techniques.
Cost: Highest; involves layered infrastructure and operational expenses.
Expertise Needed: Very High; requires deep technical understanding across multiple domains.
Decision Matrix
Choosing the right technique—or combination of techniques—is critical to building impactful and cost-effective systems. Start with the simplest approach that meets your needs, and scale up as your requirements become more sophisticated. By understanding the trade-offs of each method, you can optimize for both business value and technical feasibility.
This was very insightful and helped me to understand the nuances of implementation of the LLMs.
P.S. The hyperlink is missed here : "Read more about tools and agents here"