From 6badb5e9686ba624af0dc68743053519f953b568 Mon Sep 17 00:00:00 2001 From: Shaheen Nabi <84982228+shaheennabi@users.noreply.github.com> Date: Sun, 22 Dec 2024 09:46:04 -0800 Subject: [PATCH] Update README.md --- README.md | 24 ++++++++++++------------ 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/README.md b/README.md index b98065d..91a888b 100644 --- a/README.md +++ b/README.md @@ -32,16 +32,16 @@ I chose **Taskflow AI** because of its support for **multi-AI agent integration* ## **Project Goals** 🎯 1. **Deliver a System to Meet Client Needs** - - Create a system to provide accurate and instant destination insights, fulfilling the user's requirements seamlessly. + - Create a system to provide accurate and instant destination insights, fulfilling the user's requirements seamlessly. 2. **Build a Modular Architecture** - - Design the system to support scalability and future upgrades. + - Design the system to support scalability and future upgrades. 3. **Testing and Quality Assurance** - - Ensure the system's reliability and accuracy through rigorous testing. + - Ensure the system's reliability and accuracy through rigorous testing. 4. **Cost-Effective Measures** - - Optimize API and model-related costs without compromising performance. + - Optimize API and model-related costs without compromising performance. --- @@ -52,7 +52,7 @@ The project followed a structured approach to ensure efficient execution and hig ### **Steps in My Approach** 1. **Framework Selection** - - Chose **Taskflow AI** for its multi-AI agent support and capabilities in modular design. + - Chose **Taskflow AI** for its multi-AI agent support and capabilities in modular design. 2. **Model Selection** - Tested multiple models, including: @@ -67,14 +67,14 @@ The project followed a structured approach to ensure efficient execution and hig - Additionally, tested with **Groq inference engine**, which also failed to meet the project's performance indicators. 3. **System Design and Development** - - Built a **modular architecture** using Taskflow AI, ensuring the system could adapt to future needs and upgrades. + - Built a **modular architecture** using Taskflow AI, ensuring the system could adapt to future needs and upgrades. 4. **Cost Optimization** - - Prioritized minimizing API costs by choosing the **GPT-3.5 Turbo model** and optimizing system performance. + - Prioritized minimizing API costs by choosing the **GPT-3.5 Turbo model** and optimizing system performance. 5. **Testing and Delivery** - - Conducted rigorous testing to validate accuracy and reliability. - - Delivered the project on time, exceeding quality expectations. + - Conducted rigorous testing to validate accuracy and reliability. + - Delivered the project on time, exceeding quality expectations. --- @@ -90,13 +90,13 @@ The project faced several challenges, including: ## **How I Fixed Challenges** 🌟 - **Model API Costs**: - - Evaluated multiple models and frameworks, but **GPT-3.5 Turbo** was chosen for its cost-effectiveness and outstanding performance. + - Evaluated multiple models and frameworks, but **GPT-3.5 Turbo** was chosen for its cost-effectiveness and outstanding performance. - **Token Limitations**: - - OpenAI's **GPT-3.5 Turbo** effectively managed token constraints by leveraging its powerful reasoning capabilities, optimizing token usage, and maintaining context to generate accurate results. + - OpenAI's **GPT-3.5 Turbo** effectively managed token constraints by leveraging its powerful reasoning capabilities, optimizing token usage, and maintaining context to generate accurate results. - **Memory Challenges**: - - Compared with other models like LLaMA and Google's offerings, **GPT-3.5 Turbo** proved superior in handling memory-intensive tasks and maintaining consistent performance. + - Compared with other models like LLaMA and Google's offerings, **GPT-3.5 Turbo** proved superior in handling memory-intensive tasks and maintaining consistent performance. ---