LLM Applications: How to Leverage Generative Artificial Intelligence to Create Successful Startups.

Startups with LLM Applications are revolutionizing industries such as banking, health, insurance, education, legal, tourism, construction, logistics, marketing, sales, customer service, and even public administration. It’s not science fiction; there are already startups with LLM Applications doing things like:

• Improving surgeon efficiency.
• Public attention in banks.
• Tutoring for students.
• Legal recommendations.
• Stock market analysis.
• Conducting physiotherapy sessions.
• Finding new customers.
• Immigration procedures.
• Audits.
• Managing contracts with Public Administration.
• Construction project management.
• Event organization.
• Debt management.
• Answering (yes, answering) surveys.
• Generating news.
• Negotiating a sales contract.
• Valuing a company.
• Taking notes in meetings.

Challenges of LLM Applications in a startup.

While LLM Applications can create spectacular opportunities for startups, their utilization presents challenges that are important to consider in advance:

• Business opportunities.
• Entry barriers.
• Startup team composition.
• Investment.
• Token usage and costs.
• Context Window limits.
• Private data security.
• Misalignment: unwanted content.
• Security: injections, leaks, jailbreaking.
• Inference latency (speed vs cost).
• Multiple data sources.
• Open Source models.
• The problem of lack of reproducibility.
• Hallucinations.
• The problem of outdated data.
• A different evaluation.
• Prompts.

Program Goal.

The goal of this program is for the student to learn how to create LLM Applications in the context of a startup. For this purpose, they will be provided with:

• Theoretical content (see details below).
• Tools.
• Practical exercises.
• Expert assistance.

Prerequisites.

Basic knowledge of the Python programming language, Data Science, and Machine Learning.

Students who do not have these prerequisites can obtain them by taking our “Quick Course on Basic Knowledge in Python, Data Science, and Machine Learning.”

Program contents.

• Successful startups with LLM Applications.
• Explosion of investment in Generative AI: evolution.
• Growth cycle of startups with LLM Applications.
• Team of startups with LLM Applications.
• Alternatives for startups with LLM Applications to address the lack of technical support or an accelerator.
• What can LLM Applications do? Real cases in multiple industries and enterprise areas.
• Introduction to Generative Artificial Intelligence.
• Introduction to language models.
• Transformers: a turning point.
• Introduction to large language models.
• Foundational models and LLM applications.
• Criteria for selecting an LLM model.
• Training an LLM model from scratch.
• Fine-tuning an LLM model.
• In-context learning.
• Context window and tokens.
• RAG.
• Embeddings.
• Semantic Search.
• Vector Databases.
• Prompt Engineering.
• Orchestration Frameworks.
• Autonomous agents.
• Architecture of a basic LLM Application.
• Architecture of an advanced LLM Application.
• Lifecycle.
• Cache.
• Cloud.
• Validation.
• LLM Ops.
• App frameworks.
• No-code alternatives.
• Principles of Responsible AI.
• Cost management.
• Creation of LLM Applications from scratch.

Location and duration of the program.
• Multiple locations in United States.
• Multiple formats: part-time, full-time, in-person, and online.
• The full-time format is five intensive days, 8 hours a day, 40 hours in total.
• For large companies, in-company training option available.
• We have collaboration agreements with Business Schools, Universities, Training Centers, Business Accelerators, and Local Development Agencies.

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