diff --git a/advanced-prompting-patterns/conversation_manager.py b/advanced-prompting-patterns/conversation_manager.py
new file mode 100644
index 0000000..53792d6
--- /dev/null
+++ b/advanced-prompting-patterns/conversation_manager.py
@@ -0,0 +1,117 @@
+import os
+import tiktoken
+import json
+from openai import OpenAI
+from datetime import datetime
+
+DEFAULT_API_KEY = os.environ.get("TOGETHER_API_KEY")
+DEFAULT_BASE_URL = "https://api.together.xyz/v1"
+DEFAULT_MODEL = "meta-llama/Meta-Llama-3-8B-Instruct-Lite"
+DEFAULT_TEMPERATURE = 0.7
+DEFAULT_MAX_TOKENS = 350
+DEFAULT_TOKEN_BUDGET = 4096
+
+
+class ConversationManager:
+    def __init__(self, api_key=None, base_url=None, model=None, history_file=None, temperature=None, max_tokens=None, token_budget=None):
+        if not api_key:
+            api_key = DEFAULT_API_KEY
+        if not base_url:
+            base_url = DEFAULT_BASE_URL
+            
+        self.client = OpenAI(
+            api_key=api_key,
+            base_url=base_url
+        )
+        if history_file is None:
+            timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+            self.history_file = f"conversation_history_{timestamp}.json"
+        else:
+            self.history_file = history_file
+
+        self.model = model if model else DEFAULT_MODEL
+        self.temperature = temperature if temperature else DEFAULT_TEMPERATURE
+        self.max_tokens = max_tokens if max_tokens else DEFAULT_MAX_TOKENS
+        self.token_budget = token_budget if token_budget else DEFAULT_TOKEN_BUDGET
+
+        self.system_messages = {
+            "blogger": "You are a creative blogger specializing in engaging and informative content for GlobalJava Roasters.",
+            "social_media_expert": "You are a social media expert, crafting catchy and shareable posts for GlobalJava Roasters.",
+            "creative_assistant": "You are a creative assistant skilled in crafting engaging marketing content for GlobalJava Roasters.",
+            "custom": "Enter your custom system message here."
+        }
+        self.system_message = self.system_messages["creative_assistant"]
+        self.conversation_history = [{"role": "system", "content": self.get_system_message()}]
+
+    def count_tokens(self, text):
+        try:
+            encoding = tiktoken.encoding_for_model(self.model)
+        except KeyError:
+            encoding = tiktoken.get_encoding("cl100k_base")
+
+        tokens = encoding.encode(text)
+        return len(tokens)
+
+    def total_tokens_used(self):
+        return sum(self.count_tokens(message['content']) for message in self.conversation_history)
+    
+    def enforce_token_budget(self):
+        while self.total_tokens_used() > self.token_budget:
+            if len(self.conversation_history) <= 1:
+                break
+            self.conversation_history.pop(1)
+
+    def set_persona(self, persona):
+        if persona in self.system_messages:
+            self.system_message = self.system_messages[persona]
+            self.update_system_message_in_history()
+        else:
+            raise ValueError(f"Unknown persona: {persona}. Available personas are: {list(self.system_messages.keys())}")
+
+    def set_custom_system_message(self, custom_message):
+        if not custom_message:
+            raise ValueError("Custom message cannot be empty.")
+        self.system_messages['custom'] = custom_message
+        self.set_persona('custom')
+    
+    def get_system_message(self):
+        system_message = self.system_message
+        system_message += f"\nImportant: Tailor your response to fit within {DEFAULT_MAX_TOKENS/2} word limit\n"
+        return system_message        
+
+    def update_system_message_in_history(self):
+        if self.conversation_history and self.conversation_history[0]["role"] == "system":
+            self.conversation_history[0]["content"] = self.get_system_message()
+        else:
+            system_message = self.system_message
+            system_message += f"\nImportant: Tailor your response to fit within {DEFAULT_MAX_TOKENS/2} words limit\n"
+            self.conversation_history.insert(0, {"role": "system", "content": self.get_system_message()})
+
+    def chat_completion(self, prompt, temperature=None, max_tokens=None):
+        temperature = temperature if temperature is not None else self.temperature
+        max_tokens = max_tokens if max_tokens is not None else self.max_tokens
+
+        self.conversation_history.append({"role": "user", "content": prompt})
+
+        self.enforce_token_budget()
+
+        try:
+            response = self.client.chat.completions.create(
+                model=self.model,
+                messages=self.conversation_history,
+                temperature=temperature,
+                max_tokens=max_tokens,
+            )
+        except Exception as e:
+            print(f"An error occurred while generating a response: {e}")
+            return None
+
+        ai_response = response.choices[0].message.content
+        self.conversation_history.append({"role": "assistant", "content": ai_response})
+
+        return ai_response
+    
+    def reset_conversation_history(self):
+        self.conversation_history = [{"role": "system", "content": self.self.get_system_message()}]
+
+        
diff --git a/advanced-prompting-patterns/solution-files/campaign_generator.py b/advanced-prompting-patterns/solution-files/campaign_generator.py
new file mode 100644
index 0000000..b70549f
--- /dev/null
+++ b/advanced-prompting-patterns/solution-files/campaign_generator.py
@@ -0,0 +1,183 @@
+from conversation_manager import ConversationManager
+from prompt_templates import build_campaign_prompt, build_extract_prompt, build_draft_prompt
+from models import CampaignBrief, ExtractedProductInfo
+from utils import validate_json_output
+
+TEMPERATURE = 0.2
+
+def generate_campaign_brief(factsheet, max_retries=3):
+    """Generate a validated campaign brief with automatic repair."""
+    conversation = ConversationManager()
+
+    initial_prompt = build_campaign_prompt(factsheet)
+    response = conversation.chat_completion(initial_prompt, temperature=TEMPERATURE)
+
+    print("Initial response:")
+    print(response)
+    print()
+
+    success, result = validate_json_output(response, CampaignBrief)
+
+    if success:
+        print("✓ Valid on first attempt!")
+        return result
+
+    print(f"✗ Validation failed: {result}")
+    print()
+
+    retries = 0
+    last_error = result
+
+    while retries < max_retries:
+        print(f"Attempting repair {retries + 1}/{max_retries}...")
+
+        repair_prompt = f"""
+The JSON you provided had validation errors:
+
+{last_error}
+
+Please provide corrected JSON that fixes these errors. Remember:
+- campaign_goal must be exactly "awareness", "engagement", or "conversion"
+- All required fields must be present: campaign_name, target_audience, key_message, campaign_goal, call_to_action, channel_recommendations
+- channel_recommendations must be a list of strings
+
+Respond with valid JSON only. Keep each string value to 1–2 sentences max.
+"""
+
+        response = conversation.chat_completion(repair_prompt, temperature=TEMPERATURE)
+
+        print("Repair response:")
+        print(response)
+        print()
+
+        success, result = validate_json_output(response, CampaignBrief)
+
+        if success:
+            print("✓ Repair successful!")
+            return result
+
+        last_error = result
+        print(f"✗ Still invalid: {last_error}")
+        print()
+        retries += 1
+
+    raise ValueError(
+        f"Could not generate valid campaign brief after {max_retries} attempts. Last error: {last_error}"
+    )
+
+
+def extract_product_info(factsheet, max_retries=3):
+    """Extract structured information from product factsheet."""
+    conversation = ConversationManager()
+
+    prompt = build_extract_prompt(factsheet)
+    response = conversation.chat_completion(prompt, temperature=TEMPERATURE)
+
+    success, result = validate_json_output(response, ExtractedProductInfo)
+    if success:
+        return result
+
+    retries = 0
+    last_error = result
+
+    while retries < max_retries:
+        repair_prompt = f"""
+The JSON had errors:
+
+{last_error}
+
+Provide corrected JSON matching the required structure.
+Respond with JSON only. Keep each string value to 1–2 sentences max.
+Use [] for scarcity_factors if none.
+"""
+        response = conversation.chat_completion(repair_prompt, temperature=TEMPERATURE)
+
+        success, result = validate_json_output(response, ExtractedProductInfo)
+        if success:
+            return result
+
+        last_error = result
+        retries += 1
+
+    raise ValueError(f"Could not extract product info after {max_retries} attempts. Last error: {last_error}")
+
+
+def generate_campaign_brief_pipeline(factsheet, max_retries=3):
+    """Generate campaign brief using multi-step pipeline."""
+    print("Step 1: Extracting product information...")
+    extracted = extract_product_info(factsheet, max_retries)
+    print(f"✓ Extracted info for: {extracted.product_name}")
+
+    print("Step 2: Drafting campaign brief...")
+    conversation = ConversationManager()
+
+    prompt = build_draft_prompt(extracted)
+    response = conversation.chat_completion(prompt, temperature=TEMPERATURE)
+
+    print("Initial campaign brief response:")
+    print(response)
+    print()
+
+    success, result = validate_json_output(response, CampaignBrief)
+    if success:
+        print("✓ Valid campaign brief generated")
+        return result
+
+    print("Step 3: Repairing output...")
+    retries = 0
+    last_error = result
+
+    while retries < max_retries:
+        repair_prompt = f"""
+The JSON had validation errors:
+
+{last_error}
+
+Provide corrected JSON. Remember:
+- campaign_goal must be "awareness", "engagement", or "conversion"
+- All required fields must be present
+- channel_recommendations must be a list of strings
+
+Respond with JSON only. Keep each string value to 1–2 sentences max.
+"""
+        response = conversation.chat_completion(repair_prompt, temperature=TEMPERATURE)
+
+        success, result = validate_json_output(response, CampaignBrief)
+        if success:
+            print("✓ Repair successful")
+            return result
+
+        last_error = result
+        retries += 1
+
+    raise ValueError(f"Could not generate valid brief after {max_retries} repair attempts. Last error: {last_error}")
+
+
+if __name__ == "__main__":
+    factsheet = """
+Product: Limited Edition Geisha Reserve
+Origin: Hacienda La Esmeralda, Panama
+Altitude: 1,600-1,800 meters
+Processing: Natural, 72-hour fermentation
+Flavor Profile: Jasmine, bergamot, white peach, honey sweetness,
+silky body, complex finish with hints of tropical fruit
+Certifications: Single Estate, Competition Grade
+Limited Production: Only 500 bags produced this season
+Story: This micro-lot scored 94.1 points in the 2024 Cup of Excellence
+competition. The beans come from 30-year-old Geisha trees grown in
+volcanic soil. The extended fermentation process was developed
+specifically for this lot to enhance the floral characteristics.
+Price: $89.99/bag
+Previous Customer Feedback: "Best coffee I've ever tasted" - Coffee
+Review Magazine. Sold out in 3 days last year.
+""".strip()
+
+    try:
+        brief = generate_campaign_brief_pipeline(factsheet)
+        print("✓ Generated valid campaign brief!")
+        print(f"Campaign: {brief.campaign_name}")
+        print(f"Target: {brief.target_audience}")
+        print(f"Goal: {brief.campaign_goal.value}")
+        print(f"Channels: {', '.join(brief.channel_recommendations)}")
+    except ValueError as e:
+        print(f"✗ Failed to generate brief: {e}")
diff --git a/advanced-prompting-patterns/solution-files/conversation_manager.py b/advanced-prompting-patterns/solution-files/conversation_manager.py
new file mode 100644
index 0000000..53792d6
--- /dev/null
+++ b/advanced-prompting-patterns/solution-files/conversation_manager.py
@@ -0,0 +1,117 @@
+import os
+import tiktoken
+import json
+from openai import OpenAI
+from datetime import datetime
+
+DEFAULT_API_KEY = os.environ.get("TOGETHER_API_KEY")
+DEFAULT_BASE_URL = "https://api.together.xyz/v1"
+DEFAULT_MODEL = "meta-llama/Meta-Llama-3-8B-Instruct-Lite"
+DEFAULT_TEMPERATURE = 0.7
+DEFAULT_MAX_TOKENS = 350
+DEFAULT_TOKEN_BUDGET = 4096
+
+
+class ConversationManager:
+    def __init__(self, api_key=None, base_url=None, model=None, history_file=None, temperature=None, max_tokens=None, token_budget=None):
+        if not api_key:
+            api_key = DEFAULT_API_KEY
+        if not base_url:
+            base_url = DEFAULT_BASE_URL
+            
+        self.client = OpenAI(
+            api_key=api_key,
+            base_url=base_url
+        )
+        if history_file is None:
+            timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+            self.history_file = f"conversation_history_{timestamp}.json"
+        else:
+            self.history_file = history_file
+
+        self.model = model if model else DEFAULT_MODEL
+        self.temperature = temperature if temperature else DEFAULT_TEMPERATURE
+        self.max_tokens = max_tokens if max_tokens else DEFAULT_MAX_TOKENS
+        self.token_budget = token_budget if token_budget else DEFAULT_TOKEN_BUDGET
+
+        self.system_messages = {
+            "blogger": "You are a creative blogger specializing in engaging and informative content for GlobalJava Roasters.",
+            "social_media_expert": "You are a social media expert, crafting catchy and shareable posts for GlobalJava Roasters.",
+            "creative_assistant": "You are a creative assistant skilled in crafting engaging marketing content for GlobalJava Roasters.",
+            "custom": "Enter your custom system message here."
+        }
+        self.system_message = self.system_messages["creative_assistant"]
+        self.conversation_history = [{"role": "system", "content": self.get_system_message()}]
+
+    def count_tokens(self, text):
+        try:
+            encoding = tiktoken.encoding_for_model(self.model)
+        except KeyError:
+            encoding = tiktoken.get_encoding("cl100k_base")
+
+        tokens = encoding.encode(text)
+        return len(tokens)
+
+    def total_tokens_used(self):
+        return sum(self.count_tokens(message['content']) for message in self.conversation_history)
+    
+    def enforce_token_budget(self):
+        while self.total_tokens_used() > self.token_budget:
+            if len(self.conversation_history) <= 1:
+                break
+            self.conversation_history.pop(1)
+
+    def set_persona(self, persona):
+        if persona in self.system_messages:
+            self.system_message = self.system_messages[persona]
+            self.update_system_message_in_history()
+        else:
+            raise ValueError(f"Unknown persona: {persona}. Available personas are: {list(self.system_messages.keys())}")
+
+    def set_custom_system_message(self, custom_message):
+        if not custom_message:
+            raise ValueError("Custom message cannot be empty.")
+        self.system_messages['custom'] = custom_message
+        self.set_persona('custom')
+    
+    def get_system_message(self):
+        system_message = self.system_message
+        system_message += f"\nImportant: Tailor your response to fit within {DEFAULT_MAX_TOKENS/2} word limit\n"
+        return system_message        
+
+    def update_system_message_in_history(self):
+        if self.conversation_history and self.conversation_history[0]["role"] == "system":
+            self.conversation_history[0]["content"] = self.get_system_message()
+        else:
+            system_message = self.system_message
+            system_message += f"\nImportant: Tailor your response to fit within {DEFAULT_MAX_TOKENS/2} words limit\n"
+            self.conversation_history.insert(0, {"role": "system", "content": self.get_system_message()})
+
+    def chat_completion(self, prompt, temperature=None, max_tokens=None):
+        temperature = temperature if temperature is not None else self.temperature
+        max_tokens = max_tokens if max_tokens is not None else self.max_tokens
+
+        self.conversation_history.append({"role": "user", "content": prompt})
+
+        self.enforce_token_budget()
+
+        try:
+            response = self.client.chat.completions.create(
+                model=self.model,
+                messages=self.conversation_history,
+                temperature=temperature,
+                max_tokens=max_tokens,
+            )
+        except Exception as e:
+            print(f"An error occurred while generating a response: {e}")
+            return None
+
+        ai_response = response.choices[0].message.content
+        self.conversation_history.append({"role": "assistant", "content": ai_response})
+
+        return ai_response
+    
+    def reset_conversation_history(self):
+        self.conversation_history = [{"role": "system", "content": self.self.get_system_message()}]
+
+        
diff --git a/advanced-prompting-patterns/solution-files/models.py b/advanced-prompting-patterns/solution-files/models.py
new file mode 100644
index 0000000..bd02ff8
--- /dev/null
+++ b/advanced-prompting-patterns/solution-files/models.py
@@ -0,0 +1,27 @@
+from pydantic import BaseModel, ConfigDict
+from typing import List
+from enum import Enum
+
+class CampaignGoal(str, Enum):
+    AWARENESS = "awareness"
+    ENGAGEMENT = "engagement"
+    CONVERSION = "conversion"
+
+class CampaignBrief(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+    campaign_name: str
+    target_audience: str
+    key_message: str
+    campaign_goal: CampaignGoal
+    call_to_action: str
+    channel_recommendations: List[str]
+
+class ExtractedProductInfo(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+    product_name: str
+    origin_story: str
+    unique_features: List[str]
+    flavor_highlights: List[str]
+    certifications: List[str]
+    price_point: str
+    scarcity_factors: List[str] = []
diff --git a/advanced-prompting-patterns/solution-files/prompt_templates.py b/advanced-prompting-patterns/solution-files/prompt_templates.py
new file mode 100644
index 0000000..313bab3
--- /dev/null
+++ b/advanced-prompting-patterns/solution-files/prompt_templates.py
@@ -0,0 +1,139 @@
+CAMPAIGN_SYSTEM_V2 = """
+PROMPT_ID: campaign_brief_generator
+VERSION: 2.0
+LAST_UPDATED: 2026-01-08
+CHANGELOG:
+- v2.0: Added specific channel guidance for premium products
+- v1.1: Clarified target audience requirements
+- v1.0: Initial version
+
+You are a marketing campaign strategist for GlobalJava Roasters,
+a premium coffee company focused on quality and sustainability.
+"""
+
+CAMPAIGN_SYSTEM = """
+You are a marketing campaign strategist for GlobalJava Roasters,
+a premium coffee company focused on quality and sustainability.
+"""
+
+CAMPAIGN_TASK = """
+Create a marketing campaign brief for the product described below.
+"""
+
+CAMPAIGN_CONSTRAINTS = """
+Follow these rules:
+- Base all claims on the provided product information
+- Use professional but engaging language appropriate for coffee enthusiasts
+- Focus on the product's unique characteristics and value proposition
+- Recommend marketing channels suitable for premium coffee consumers
+"""
+
+CAMPAIGN_OUTPUT = """
+Output your response as valid JSON with this structure:
+
+{{
+  "campaign_name": "string",
+  "target_audience": "string",
+  "key_message": "string",
+  "campaign_goal": "awareness" | "engagement" | "conversion",
+  "call_to_action": "string",
+  "channel_recommendations": ["string", "string", ...]
+}}
+
+Respond with JSON only. No explanations or markdown. Keep each string value to 1–2 sentences max.
+"""
+
+def build_campaign_prompt(factsheet):
+    """Build a campaign brief prompt from template blocks."""
+    reference = f"Product Information:\n{factsheet}"
+
+    return f"""
+{CAMPAIGN_SYSTEM}
+
+{CAMPAIGN_TASK}
+
+{CAMPAIGN_CONSTRAINTS}
+
+{reference}
+
+{CAMPAIGN_OUTPUT}
+""".strip()
+
+EXTRACT_SYSTEM = "You are a data extraction specialist."
+
+EXTRACT_TASK = """
+Extract key marketing-relevant information from the product factsheet below.
+Focus on facts that would matter for a marketing campaign.
+"""
+
+EXTRACT_OUTPUT = """
+Output JSON with this structure:
+{{
+  "product_name": "string",
+  "origin_story": "string",
+  "unique_features": ["string", "string", ...],
+  "flavor_highlights": ["string", "string", ...],
+  "certifications": ["string", "string", ...],
+  "price_point": "budget" | "mid-range" | "premium" | "luxury",
+  "scarcity_factors": ["string", ...] // use [] if none
+}}
+
+Respond with JSON only.
+"""
+
+def build_extract_prompt(factsheet):
+    reference = f"Product Factsheet:\n{factsheet}"
+    return f"""
+{EXTRACT_SYSTEM}
+
+{EXTRACT_TASK}
+
+{reference}
+
+{EXTRACT_OUTPUT}
+""".strip()
+
+
+DRAFT_CAMPAIGN_SYSTEM = """
+You are a marketing campaign strategist for GlobalJava Roasters.
+"""
+
+DRAFT_CAMPAIGN_TASK = """
+Create a compelling marketing campaign brief using the extracted
+product information provided below.
+"""
+
+DRAFT_CAMPAIGN_CONSTRAINTS = """
+Guidelines:
+- Craft a campaign name that captures the product's essence
+- Target audience should reflect the price point and product characteristics
+- Key message should highlight the most compelling unique features
+- Choose an appropriate campaign goal based on product positioning
+- Create a clear, actionable call-to-action
+- Recommend channels that reach premium coffee enthusiasts
+"""
+
+def build_draft_prompt(extracted_info):
+    # Format the extracted info nicely
+    info_text = f"""
+Product: {extracted_info.product_name}
+Origin Story: {extracted_info.origin_story}
+Unique Features: {', '.join(extracted_info.unique_features)}
+Flavor Highlights: {', '.join(extracted_info.flavor_highlights)}
+Price Point: {extracted_info.price_point}
+"""
+    if extracted_info.scarcity_factors:
+        info_text += f"Scarcity: {', '.join(extracted_info.scarcity_factors)}\n"
+
+    return f"""
+{DRAFT_CAMPAIGN_SYSTEM}
+
+{DRAFT_CAMPAIGN_TASK}
+
+{DRAFT_CAMPAIGN_CONSTRAINTS}
+
+Extracted Product Information:
+{info_text}
+
+{CAMPAIGN_OUTPUT}
+""".strip()
\ No newline at end of file
diff --git a/advanced-prompting-patterns/solution-files/test_prompts.py b/advanced-prompting-patterns/solution-files/test_prompts.py
new file mode 100644
index 0000000..ee61373
--- /dev/null
+++ b/advanced-prompting-patterns/solution-files/test_prompts.py
@@ -0,0 +1,48 @@
+from campaign_generator import generate_campaign_brief
+from models import CampaignGoal
+
+def test_basic_generation():
+    """Can we generate a valid brief at all?"""
+    factsheet = """
+    Product: House Blend Medium Roast
+    Origin: Colombia and Brazil blend
+    Flavor: Balanced, chocolatey, nutty
+    Price: $12.99/bag
+    """
+
+    brief = generate_campaign_brief(factsheet)
+
+    assert brief.campaign_name, "Campaign name missing"
+    assert len(brief.campaign_name) >= 5, "Campaign name too short"
+    assert brief.target_audience, "Target audience missing"
+    assert brief.campaign_goal in CampaignGoal, "Invalid campaign goal"
+    assert len(brief.channel_recommendations) > 0, "No channels recommended"
+
+    print("✓ Basic generation test passed")
+
+def test_premium_product_targeting():
+    """Premium products should target sophisticated audiences."""
+    factsheet = """
+    Product: Single Origin Ethiopian Yirgacheffe
+    Origin: Gedeb region, Ethiopia
+    Processing: Washed
+    Flavor: Floral, citrus, tea-like
+    Certifications: Organic, Fair Trade
+    Price: $24.99/bag
+    """
+
+    brief = generate_campaign_brief(factsheet)
+
+    target = brief.target_audience.lower()
+    sophisticated_terms = ['enthusiast', 'connoisseur', 'specialty', 'premium', 'aficionado']
+
+    assert any(term in target for term in sophisticated_terms), \
+        f"Premium product should target sophisticated audience, got: {brief.target_audience}"
+
+    print("✓ Premium targeting test passed")
+
+if __name__ == "__main__":
+    print("Running prompt tests...\n")
+    test_basic_generation()
+    test_premium_product_targeting()
+    print("\n✓ All tests passed!")
diff --git a/advanced-prompting-patterns/solution-files/utils.py b/advanced-prompting-patterns/solution-files/utils.py
new file mode 100644
index 0000000..d6132fb
--- /dev/null
+++ b/advanced-prompting-patterns/solution-files/utils.py
@@ -0,0 +1,26 @@
+import json
+from pydantic import ValidationError
+
+def validate_json_output(response_text, model_class):
+    """
+    Parse JSON from LLM response and validate against Pydantic model.
+    Returns (success, result_or_errors)
+    """
+    try:
+        # Extract JSON (handles markdown code blocks if present)
+        json_text = response_text.strip()
+        if json_text.startswith("```json"):
+            json_text = json_text.split("```json")[1].split("```")[0].strip()
+        elif json_text.startswith("```"):
+            json_text = json_text.split("```")[1].split("```")[0].strip()
+
+        # Parse and validate
+        data = json.loads(json_text)
+        validated = model_class(**data)
+        return True, validated
+
+    except json.JSONDecodeError as e:
+        return False, f"JSON parsing error: {str(e)}"
+
+    except ValidationError as e:
+        return False, f"Validation errors: {e.errors()}"
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