Replace GPT and Claude models to latest, better and cheaper models (#118)

* Updated GPT and Claude models to latest, better and cheaper models

* updated more files to use GPT-5 and Sonnet/Haiku 4.5 because theu are the latest, cheaper and better models

plugins/llm-application-dev/agents/ai-engineer.md

@@ -13,7 +13,7 @@ Expert AI engineer specializing in LLM application development, RAG systems, and
 
 ### LLM Integration & Model Management
 - OpenAI GPT-4o/4o-mini, o1-preview, o1-mini with function calling and structured outputs
-- Anthropic Claude 3.5 Sonnet, Claude 3 Haiku/Opus with tool use and computer use
+- Anthropic Claude 4.5 Sonnet/Haiku, Claude 4.1 Opus with tool use and computer use
 - Open-source models: Llama 3.1/3.2, Mixtral 8x7B/8x22B, Qwen 2.5, DeepSeek-V2
 - Local deployment with Ollama, vLLM, TGI (Text Generation Inference)
 - Model serving with TorchServe, MLflow, BentoML for production deployment
@@ -68,7 +68,7 @@ Expert AI engineer specializing in LLM application development, RAG systems, and
 - Observability: logging, metrics, tracing with LangSmith, Phoenix, Weights & Biases
 
 ### Multimodal AI Integration
-- Vision models: GPT-4V, Claude 3 Vision, LLaVA, CLIP for image understanding
+- Vision models: GPT-4V, Claude 4 Vision, LLaVA, CLIP for image understanding
 - Audio processing: Whisper for speech-to-text, ElevenLabs for text-to-speech
 - Document AI: OCR, table extraction, layout understanding with models like LayoutLM
 - Video analysis and processing for multimedia applications
@@ -111,7 +111,7 @@ Expert AI engineer specializing in LLM application development, RAG systems, and
 - Balances cutting-edge techniques with proven, stable solutions
 
 ## Knowledge Base
-- Latest LLM developments and model capabilities (GPT-4o, Claude 3.5, Llama 3.2)
+- Latest LLM developments and model capabilities (GPT-4o, Claude 4.5, Llama 3.2)
 - Modern vector database architectures and optimization techniques
 - Production AI system design patterns and best practices
 - AI safety and security considerations for enterprise deployments

plugins/llm-application-dev/agents/prompt-engineer.md

@@ -53,7 +53,7 @@ Expert prompt engineer specializing in advanced prompting methodologies and LLM
 - Multi-turn conversation management
 - Image and multimodal prompt engineering
 
-#### Anthropic Claude (3.5 Sonnet, Haiku, Opus)
+#### Anthropic Claude (4.5 Sonnet, Haiku, Opus)
 - Constitutional AI alignment with Claude's training
 - Tool use optimization for complex workflows
 - Computer use prompting for automation tasks

plugins/llm-application-dev/commands/prompt-optimize.md

@@ -113,7 +113,7 @@ Final Response: [Refined]
 
 ### 5. Model-Specific Optimization
 
-**GPT-4/GPT-4o**
+**GPT-5/GPT-4o**
 ```python
 gpt4_optimized = """
 ##CONTEXT##
@@ -136,7 +136,7 @@ gpt4_optimized = """
 """
 ```
 
-**Claude 3.5/4**
+**Claude 4.5/4**
 ```python
 claude_optimized = """
 <context>
@@ -566,7 +566,7 @@ testing_recommendations:
   metrics: ["accuracy", "satisfaction", "cost"]
 
 deployment_strategy:
-  model: "GPT-4 for quality, Claude for safety"
+  model: "GPT-5 for quality, Claude for safety"
   temperature: 0.7
   max_tokens: 2000
   monitoring: "Track success, latency, feedback"

plugins/llm-application-dev/skills/llm-evaluation/SKILL.md

@@ -186,7 +186,7 @@ def calculate_factuality(claim, knowledge_base):
 ### Single Output Evaluation
 ```python
 def llm_judge_quality(response, question):
-    """Use GPT-4 to judge response quality."""
+    """Use GPT-5 to judge response quality."""
     prompt = f"""Rate the following response on a scale of 1-10 for:
 1. Accuracy (factually correct)
 2. Helpfulness (answers the question)
@@ -205,7 +205,7 @@ Provide ratings in JSON format:
 """
 
     result = openai.ChatCompletion.create(
-        model="gpt-4",
+        model="gpt-5",
         messages=[{"role": "user", "content": prompt}],
         temperature=0
     )
@@ -236,7 +236,7 @@ Answer with JSON:
 """
 
     result = openai.ChatCompletion.create(
-        model="gpt-4",
+        model="gpt-5",
         messages=[{"role": "user", "content": prompt}],
         temperature=0
     )

plugins/llm-application-dev/skills/prompt-engineering-patterns/references/chain-of-thought.md

@@ -65,7 +65,7 @@ def self_consistency_cot(query, n=5, temperature=0.7):
     responses = []
     for _ in range(n):
         response = openai.ChatCompletion.create(
-            model="gpt-4",
+            model="gpt-5",
             messages=[{"role": "user", "content": prompt}],
             temperature=temperature
         )