Driving Profit with LLMs and RAG: Practical Applications and Case studies

Driving Profit with LLMs and RAG: Practical Applications and Case studies

The cost of implementing Large Language Models (LLMs) like Chat-GPT and Retrieval-Augmented Generation (RAG) at scale has proven to be prohibitive for many businesses. So, what use cases can be implemented that generate a profit for companies?

LLMs leverage advanced algorithms to process and generate human-like text based on large datasets. RAGs enhance LLMs by incorporating custom data to provide more specialized responses. The algorithms behind LLMs and RAGs are great at creating average responses or identifying patterns in the data provided, including outliers. However, with an average minimum cost of $0.10 per call to the LLM, real-time feedback in live systems can be cost-prohibitive in large-scale production systems.?

10 Profitable Use Cases for LLMs and RAG:

1. Identifying Unusual Patterns in Written Documentation:

• Example: Editing documents, recommending changes, or analyzing legal documents to highlight non-standard clauses.

2. Educational Tools:

• Example: Assessing student understanding and providing feedback summaries to teachers as part of batch-processing.

3. Data Entry Automation:

• Example: Extracting information from documents like medical records or receipts, and inputting it into systems, especially in large-scale migrations from one system to another. For instance, this could involve transferring written medical records into electronic systems. This process ensures efficient data management and accessibility, facilitating tasks such as retrieving specific details like the latest receipt from vendor W, total expenditures at Vendor W, or the most recent diagnosis or bloodwork for patient Q.

4. Task Management Applications:

• Example: Analyzing responses against company procedures and making project recommendations based on employee roles and datasets.

5. Customer Support Chatbots:?

• Example: Providing 24/7 responses on product details, services, and company policies.

6. Content Generation:

• Example: While LLMs excel as content generators, many users find that they are not exceptional at creating entirely original content or crafting unique pieces that haven't been previously written about or created elsewhere. However, if generating average responses suffices for the required content, LLMs serve as excellent tools for creating blog posts, social media updates, and marketing materials.?

7. Customer Follow-ups:

• Example: Generating follow-up emails and sorting initial inquiries efficiently.

8. Summarization for Customer Service Improvement:

• Example: Providing managers with summarized communications before handling customer service issues.

9. Product Recommendations:

• Example: Providing personalized recommendations to customers based on current offerings. Additionally, the LLM can analyze chat conversations to identify goods/services that customers express interest in, which are closely related to existing offerings and feasible for the business to provide.

10. Email Escalation Analysis:

• Example: Prioritizing and escalating emails that require supervisor attention in customer support.

Industries and Affordable Use Cases:

1. Retail: Customer service chatbots, content marketing generation, customer inquiries, email escalation analysis, and conversation summaries.

2. Medical: Document processing, diagnostic summaries, and patient file inquiries.

3. Technology Development: Customer service automation, customer inquiries, conversation summaries, content creation, and email management.

4. Utilities: Customer support automation, content marketing generation, customer inquiries and conversation analysis.

5. Private Equity/Finance/Investing: Summarizing investment opportunities and identifying any aspects that might have been overlooked using standard formats. Creating investor outreach content. Implementing a customer service chatbot capable of answering queries regarding performance and prospectuses. Managing email escalations. Conducting non-real-time risk assessments for conversations, written documents, or investment deals.

6. Government: Public service chatbots, email responses, and document processing.

7. Legal: Contract analysis, document summaries, and legal clause identification.

8. Manufacturing: Assisting workers with training using manuals for the machines. Developing customer service or retail chatbots. Analyzing customer feedback to identify both outlier and average responses. Monitoring and analyzing compliance data.

9. Travel and Hospitality: Customer service automation, feedback analysis, content creation, and email management.

LLM Use Cases That Can Be Cost-Prohibitive:

While Large Language Models (LLMs) offer numerous benefits, their implementation can incur significant costs, particularly in certain use cases. Here are some areas where costs are be prohibitively high:

1. Custom Training on Proprietary Data:

• Description: Training LLMs on proprietary data for specialized applications.
• Costs: High computational resources, specialized hardware, and extensive time investment.

2. Real-time High-Volume Customer Support:

• Description: Deploying LLMs for real-time customer interactions at scale.
• Costs: Continuous cloud computing expenses, low latency requirements, and high availability demands.

3. Comprehensive Legal Document Analysis:

• Description: Using LLMs for precise legal document review and drafting.
• Costs: High due to precision needs and extensive data processing.

4. Sophisticated Autonomous Systems:

• Description: Integrating LLMs into autonomous systems like self-driving cars.
• Costs: High computational power, safety compliance, and liability considerations.

5. Large-scale Data Processing and Analysis:

• Description: Real-time analysis for financial trading or fraud detection.
• Costs: Requires robust infrastructure, constant updates, and potential high error costs.

6. Enterprise-level Conversational AI:

• Description: Developing complex AI systems for enterprise communication.
• Costs: Extensive development, integration, and maintenance expenses.

7. Multilingual and Multimodal Applications:

• Description: Supporting multiple languages and data types (text, voice, images).
• Costs: High resource demand for training and data consistency across modes.

8. Highly Customized Virtual Assistants:

• Description: Building tailored virtual assistants for specific business needs.
• Costs: Requires deep integration, ongoing customization, and maintenance.

9. Long-term Data Storage and Privacy Compliance:

• Description: Securely storing and managing large volumes of data processed by LLMs.
• Costs: Investments in secure storage, data encryption, and regulatory compliance.

Case Study

A retail company aims to optimize customer service operations and reduce training costs. The call center handles an average of 4,400 calls per month, typical for its size, with each call involving approximately 6 interactions between customers and agents, totaling 24,600 API calls monthly for real-time training and feedback.

Cost-prohibitive use case:

Real-time training and feedback between customer service agents and customers involves 24,600 API calls at $0.002 per token. The average length of a return policy for a company is 3,272 words. Each interaction averages 364 tokens for policy and language translation prompts, rising to 1,213 tokens for calls requiring supervisor escalation (10% of calls). Using the cheapest model costs $22,084 monthly, reduced to $13,250 with a typical 30-40% enterprise discount. Following optimization through RAG and prompt engineering, costs drop by 70% to approximately $6,625 per month, assuming an expert AI engineer's involvement with an additional $75,000 salary over three months or a one-time $400,000 fee for a development firm's implementation.

Affordable use case:

Batch processing of archived customer service calls to generate up-to date manuals and analyze complex customer service trends. Conversation analysis of common escalation topics and assessing predictors for the performance of both top-performing and lowest-performing customer service agents.

This involves processing over a million words in approximately 3 API calls per report, costing approximately $31.80 per call or $95.40 per report. Monthly usage at weekly intervals totals about $381.60, potentially reduced further with enterprise agreements or third-party software solutions. A 70% cost-optimization through RAG and prompt engineering can be achieved on average. In-house development requires a three-month investment for an expert AI engineer or development firm.

Summary

By aligning practical use cases with industry-specific needs and navigating cost challenges effectively, businesses can drive profitability through strategic implementation of LLMs and RAG.?

This integrated approach ties the practical applications and profitability of LLMs and RAG together, highlighting their versatility and challenges across various industries.

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