RAG Architecture for HR Applications: Building Context-Aware Interview Systems

Introduction: The RAG Revolution in HR Tech

Retrieval-Augmented Generation (RAG) represents a paradigm shift in how AI systems access and utilize information. For HR applications—particularly AI-powered interviews—RAG solves a critical problem: how can an AI conduct role-specific, context-aware conversations without requiring manual programming for every job type?

Having implemented RAG architecture in a production interview platform, I'll share technical insights, architectural decisions, and lessons learned from deploying RAG in the HR domain.

Why RAG Matters for HR Applications

The Traditional Approach's Limitations

Traditional AI interview systems use one of two approaches:

1. Rule-Based Systems:

# Rigid, manually programmed
if job_title == "Software Engineer":
    ask_question("Tell me about your experience with Python")
elif job_title == "Marketing Manager":
    ask_question("Describe your campaign management experience")

Problems:

• Requires manual configuration for every role
• Cannot adapt to unique job requirements
• Fails when job descriptions don't match templates
• Cannot incorporate company-specific context

2. Pure LLM Approach:

# Uses LLM's training knowledge only
prompt = f"Interview a candidate for {job_title}"
response = llm.generate(prompt)

Problems:

• Hallucinates job requirements
• No access to specific job description
• Cannot reference company policies or values
• Inconsistent across similar roles

RAG's Solution

RAG combines retrieval systems with language models, allowing AI to:

1. Access specific job requirements dynamically
2. Reference company policies and culture documents
3. Incorporate industry-specific knowledge
4. Adapt questions based on candidate background
5. Provide consistent, contextual interviews

RAG Architecture for Interview Systems

High-Level Architecture

User Input (Candidate Response)
    ↓
Query Embedding
    ↓
Vector Database Search
    ↓
Relevant Context Retrieval
    ↓
Context + Query → LLM
    ↓
Generated Follow-up Question
    ↓
Response to Candidate

Let's examine each component in depth.

Component 1: Document Processing and Indexing

Input Documents for HR RAG

Our system ingests multiple document types:

1. Job Descriptions

• Required skills and experience
• Responsibilities and expectations
• Team structure and reporting
• Technical requirements

2. Company Knowledge Base

• Company values and mission
• Team culture documents
• Product/service information
• Work environment details

3. Interview Guidelines

• Evaluation criteria
• Legal compliance requirements
• Behavioral indicators
• Red flags to watch for

4. Role-Specific Resources

• Technical documentation (for tech roles)
• Industry knowledge bases
• Common scenarios and challenges
• Success profiles from high performers

Document Processing Pipeline

from langchain.document_loaders import (
    PyPDFLoader,
    UnstructuredWordDocumentLoader,
    TextLoader
)
from langchain.text_splitter import RecursiveCharacterTextSplitter

class HRDocumentProcessor:
    def __init__(self):
        self.text_splitter = RecursiveCharacterTextSplitter(
            chunk_size=500,  # Optimized for interview context
            chunk_overlap=50,
            separators=["\n\n", "\n", ". ", " ", ""]
        )

    def process_job_description(self, file_path):
        """Process job description into chunks"""

        # Load document
        if file_path.endswith('.pdf'):
            loader = PyPDFLoader(file_path)
        elif file_path.endswith('.docx'):
            loader = UnstructuredWordDocumentLoader(file_path)
        else:
            loader = TextLoader(file_path)

        documents = loader.load()

        # Extract structured information
        structured_data = self.extract_structured_info(documents[0].page_content)

        # Split into chunks
        chunks = self.text_splitter.split_documents(documents)

        # Enrich chunks with metadata
        enriched_chunks = self.add_metadata(chunks, structured_data)

        return enriched_chunks

    def extract_structured_info(self, text):
        """Extract structured data from job description"""

        # Use NER or LLM to extract key fields
        return {
            'job_title': self.extract_job_title(text),
            'required_skills': self.extract_skills(text),
            'experience_level': self.extract_experience_level(text),
            'department': self.extract_department(text),
            'location': self.extract_location(text)
        }

    def add_metadata(self, chunks, structured_data):
        """Add metadata to chunks for better retrieval"""

        for chunk in chunks:
            chunk.metadata.update({
                'job_title': structured_data['job_title'],
                'document_type': 'job_description',
                'section': self.identify_section(chunk.page_content),
                'importance': self.score_importance(chunk.page_content)
            })

        return chunks

Intelligent Chunking Strategy

Chunk size significantly impacts RAG performance. For interview context:

Too Small (< 200 tokens):

• Loses context
• Requires more retrieval calls
• Fragmented information

Too Large (> 1000 tokens):

• Exceeds context window quickly
• Includes irrelevant information
• Slower retrieval

Our Optimized Approach:

class AdaptiveChunker:
    def chunk_by_semantic_coherence(self, text):
        """Chunk based on semantic boundaries, not just length"""

        # Identify semantic boundaries
        sentences = self.split_into_sentences(text)

        chunks = []
        current_chunk = []
        current_length = 0

        for sentence in sentences:
            sentence_embedding = self.get_embedding(sentence)

            if current_chunk:
                # Check semantic similarity with current chunk
                chunk_embedding = self.get_embedding(' '.join(current_chunk))
                similarity = cosine_similarity(sentence_embedding, chunk_embedding)

                # Start new chunk if semantic break or length limit
                if similarity < 0.7 or current_length + len(sentence) > 500:
                    chunks.append(' '.join(current_chunk))
                    current_chunk = [sentence]
                    current_length = len(sentence)
                else:
                    current_chunk.append(sentence)
                    current_length += len(sentence)
            else:
                current_chunk = [sentence]
                current_length = len(sentence)

        if current_chunk:
            chunks.append(' '.join(current_chunk))

        return chunks

This adaptive approach improved retrieval relevance by 23% compared to fixed-size chunking.

Component 2: Embedding and Vector Storage

Embedding Model Selection

We tested multiple embedding models for HR text:

Model	Dimension	Performance	Cost
OpenAI text-embedding-ada-002	1536	Excellent	$0.0001/1K tokens
sentence-transformers/all-MiniLM-L6-v2	384	Good	Free (self-hosted)
sentence-transformers/all-mpnet-base-v2	768	Very Good	Free (self-hosted)
Cohere embed-english-v3.0	1024	Excellent	$0.0001/1K tokens

Our Choice: OpenAI ada-002 for production (quality and reliability), with MiniLM for development/testing.

from openai import OpenAI
import numpy as np

class EmbeddingGenerator:
    def __init__(self):
        self.client = OpenAI()
        self.model = "text-embedding-ada-002"

    def generate_embedding(self, text):
        """Generate embedding for text"""

        # Preprocess text
        text = self.preprocess(text)

        # Generate embedding
        response = self.client.embeddings.create(
            model=self.model,
            input=text
        )

        return np.array(response.data[0].embedding)

    def batch_generate_embeddings(self, texts, batch_size=100):
        """Generate embeddings in batches for efficiency"""

        embeddings = []

        for i in range(0, len(texts), batch_size):
            batch = texts[i:i+batch_size]

            response = self.client.embeddings.create(
                model=self.model,
                input=batch
            )

            batch_embeddings = [
                np.array(item.embedding) 
                for item in response.data
            ]
            embeddings.extend(batch_embeddings)

        return embeddings

    def preprocess(self, text):
        """Preprocess text before embedding"""

        # Remove excessive whitespace
        text = ' '.join(text.split())

        # Truncate if too long (ada-002 limit: 8191 tokens)
        if len(text) > 8000:
            text = text[:8000]

        return text

Vector Database Selection

We evaluated several vector databases:

Pinecone:

• Pros: Fully managed, excellent performance, simple API
• Cons: Cost, vendor lock-in
• Best for: Production systems with high query volume

Weaviate:

• Pros: Self-hosted option, built-in filtering, good documentation
• Cons: More complex setup
• Best for: Complex filtering requirements

Chroma:

• Pros: Lightweight, easy to start, local development
• Cons: Not ideal for large-scale production
• Best for: Development and prototyping

FAISS:

• Pros: Extremely fast, free, battle-tested
• Cons: No filtering, requires custom infrastructure
• Best for: Large-scale with custom infrastructure

Our Implementation (Pinecone):

import pinecone
from typing import List, Dict

class VectorStore:
    def __init__(self, index_name: str):
        pinecone.init(
            api_key=os.getenv("PINECONE_API_KEY"),
            environment=os.getenv("PINECONE_ENV")
        )

        # Create index if doesn't exist
        if index_name not in pinecone.list_indexes():
            pinecone.create_index(
                name=index_name,
                dimension=1536,  # ada-002 dimension
                metric="cosine",
                pod_type="p1"
            )

        self.index = pinecone.Index(index_name)

    def upsert_documents(self, chunks: List[Dict]):
        """Insert document chunks into vector database"""

        vectors = []
        for chunk in chunks:
            vectors.append({
                'id': chunk['id'],
                'values': chunk['embedding'],
                'metadata': {
                    'text': chunk['text'],
                    'job_title': chunk.get('job_title'),
                    'document_type': chunk.get('document_type'),
                    'section': chunk.get('section'),
                    'importance': chunk.get('importance', 0.5)
                }
            })

        # Batch upsert for efficiency
        batch_size = 100
        for i in range(0, len(vectors), batch_size):
            batch = vectors[i:i+batch_size]
            self.index.upsert(vectors=batch)

    def query(
        self, 
        query_embedding: List[float], 
        top_k: int = 5,
        filter_dict: Dict = None
    ):
        """Query vector database"""

        results = self.index.query(
            vector=query_embedding,
            top_k=top_k,
            filter=filter_dict,
            include_metadata=True
        )

        return results

Hybrid Search: Combining Dense and Sparse Retrieval

Pure vector search sometimes misses exact keyword matches. Hybrid approach combines:

• Dense retrieval (embeddings): Semantic similarity
• Sparse retrieval (BM25): Keyword matching

from rank_bm25 import BM25Okapi
import numpy as np

class HybridRetriever:
    def __init__(self, vector_store, documents):
        self.vector_store = vector_store
        self.documents = documents

        # Build BM25 index
        tokenized_docs = [doc.split() for doc in documents]
        self.bm25 = BM25Okapi(tokenized_docs)

    def retrieve(
        self, 
        query: str, 
        top_k: int = 5,
        alpha: float = 0.7  # Weight for vector search vs BM25
    ):
        """Hybrid retrieval combining vector and keyword search"""

        # Vector search
        query_embedding = self.embedding_gen.generate_embedding(query)
        vector_results = self.vector_store.query(query_embedding, top_k=top_k*2)

        # BM25 search
        tokenized_query = query.split()
        bm25_scores = self.bm25.get_scores(tokenized_query)

        # Combine scores
        final_scores = {}

        for result in vector_results.matches:
            doc_id = result.id
            vector_score = result.score

            # Normalize BM25 score
            bm25_score = bm25_scores[int(doc_id)] / max(bm25_scores)

            # Weighted combination
            final_scores[doc_id] = (
                alpha * vector_score + (1 - alpha) * bm25_score
            )

        # Sort by combined score and return top k
        ranked_docs = sorted(
            final_scores.items(), 
            key=lambda x: x[1], 
            reverse=True
        )[:top_k]

        return [self.documents[int(doc_id)] for doc_id, _ in ranked_docs]

Component 3: Query Construction and Retrieval

Context-Aware Query Generation

Simple keyword queries don't capture interview context. We generate enhanced queries:

class QueryEnhancer:
    def enhance_query_with_context(
        self, 
        candidate_response: str,
        conversation_history: List[str],
        job_context: Dict
    ) -> str:
        """Enhance query with conversation and job context"""

        # Extract key entities from candidate response
        entities = self.extract_entities(candidate_response)

        # Identify topics that need probing
        topics_to_probe = self.identify_probe_topics(
            candidate_response, 
            conversation_history
        )

        # Build context-enriched query
        query = f"""
        Job Title: {job_context['title']}

        Candidate mentioned: {', '.join(entities)}

        Topics to explore: {', '.join(topics_to_probe)}

        Recent conversation:
        {self.summarize_recent_context(conversation_history[-3:])}

        What specific follow-up questions should I ask to evaluate:
        - Technical depth in mentioned areas
        - Relevance to job requirements
        - Areas needing clarification
        """

        return query

    def extract_entities(self, text):
        """Extract key entities using NER"""

        # Use spaCy or similar for NER
        doc = nlp(text)

        entities = []
        for ent in doc.ents:
            if ent.label_ in ['ORG', 'PRODUCT', 'TECHNOLOGY', 'SKILL']:
                entities.append(ent.text)

        return entities

Multi-Query Retrieval

Single queries may miss relevant context. Generate multiple queries:

class MultiQueryRetriever:
    def generate_multiple_queries(self, original_query: str) -> List[str]:
        """Generate multiple perspectives on the same query"""

        prompt = f"""
        Given this interview context query:
        {original_query}

        Generate 3 alternative phrasings that capture different aspects:
        1. A query focusing on technical requirements
        2. A query focusing on behavioral indicators
        3. A query focusing on cultural fit

        Return only the 3 queries, one per line.
        """

        response = self.llm.generate(prompt)
        queries = response.split('\n')

        return [original_query] + queries

    def retrieve_with_multiple_queries(self, queries: List[str], top_k: int = 3):
        """Retrieve using multiple queries and deduplicate"""

        all_results = []
        seen_ids = set()

        for query in queries:
            query_embedding = self.embedding_gen.generate_embedding(query)
            results = self.vector_store.query(query_embedding, top_k=top_k)

            for result in results.matches:
                if result.id not in seen_ids:
                    all_results.append(result)
                    seen_ids.add(result.id)

        # Re-rank by relevance
        reranked = self.rerank_results(all_results, queries[0])

        return reranked[:top_k * len(queries)]

Re-Ranking Retrieved Results

Initial retrieval may return suboptimal ordering. Re-rank using cross-encoder:

from sentence_transformers import CrossEncoder

class ResultReranker:
    def __init__(self):
        self.cross_encoder = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')

    def rerank(self, query: str, documents: List[str]) -> List[str]:
        """Rerank documents using cross-encoder"""

        # Create query-document pairs
        pairs = [[query, doc] for doc in documents]

        # Score pairs
        scores = self.cross_encoder.predict(pairs)

        # Sort by score
        ranked_indices = np.argsort(scores)[::-1]

        return [documents[i] for i in ranked_indices]

Component 4: Context Integration with LLM

Prompt Engineering for Interview Context

How you present retrieved context to the LLM dramatically affects response quality:

class InterviewPromptBuilder:
    def build_prompt(
        self,
        candidate_response: str,
        retrieved_contexts: List[str],
        job_requirements: Dict,
        conversation_history: List[str]
    ) -> str:
        """Build comprehensive prompt for LLM"""

        prompt = f"""
        You are conducting an AI-powered interview for the position of {job_requirements['title']}.

        ## Job Context
        {self.format_job_requirements(job_requirements)}

        ## Retrieved Relevant Information
        {self.format_retrieved_contexts(retrieved_contexts)}

        ## Conversation So Far
        {self.format_conversation_history(conversation_history)}

        ## Candidate's Latest Response
        "{candidate_response}"

        ## Your Task
        Based on the candidate's response and the relevant job requirements above:

        1. Evaluate the response against job requirements
        2. Identify areas that need deeper exploration
        3. Generate 1-2 specific follow-up questions that:
           - Probe technical depth where candidate showed knowledge
           - Clarify vague or incomplete statements
           - Assess cultural fit and soft skills
           - Are natural and conversational in tone

        ## Important Guidelines
        - Ask specific, not generic questions
        - Build on what candidate just said
        - One question at a time
        - Keep questions concise
        - Sound natural and conversational

        Your follow-up question:
        """

        return prompt

    def format_job_requirements(self, requirements: Dict) -> str:
        """Format job requirements for prompt"""

        return f"""
        Title: {requirements['title']}
        Key Skills: {', '.join(requirements['required_skills'])}
        Experience Level: {requirements['experience_level']}
        Key Responsibilities: {requirements['responsibilities']}
        """

    def format_retrieved_contexts(self, contexts: List[str]) -> str:
        """Format retrieved contexts"""

        formatted = []
        for i, context in enumerate(contexts, 1):
            formatted.append(f"[Context {i}]\n{context}\n")

        return '\n'.join(formatted)

Context Window Management

LLMs have token limits. Manage context carefully:

import tiktoken

class ContextWindowManager:
    def __init__(self, model_name="gpt-4", max_tokens=8000):
        self.encoding = tiktoken.encoding_for_model(model_name)
        self.max_tokens = max_tokens
        self.response_buffer = 1000  # Reserve for LLM response

    def count_tokens(self, text: str) -> int:
        """Count tokens in text"""
        return len(self.encoding.encode(text))

    def fit_context_to_window(
        self,
        base_prompt: str,
        retrieved_contexts: List[str],
        conversation_history: List[str]
    ) -> str:
        """Fit all context within token limit"""

        # Calculate token budgets
        base_tokens = self.count_tokens(base_prompt)
        available_tokens = self.max_tokens - base_tokens - self.response_buffer

        # Allocate tokens
        context_budget = int(available_tokens * 0.6)
        history_budget = int(available_tokens * 0.4)

        # Trim retrieved contexts
        trimmed_contexts = self.trim_to_budget(
            retrieved_contexts,
            context_budget
        )

        # Trim conversation history (keep most recent)
        trimmed_history = self.trim_history_to_budget(
            conversation_history,
            history_budget
        )

        # Build final prompt
        final_prompt = self.assemble_prompt(
            base_prompt,
            trimmed_contexts,
            trimmed_history
        )

        return final_prompt

    def trim_to_budget(
        self, 
        texts: List[str], 
        budget: int
    ) -> List[str]:
        """Trim texts to fit within token budget"""

        trimmed = []
        current_tokens = 0

        for text in texts:
            text_tokens = self.count_tokens(text)

            if current_tokens + text_tokens <= budget:
                trimmed.append(text)
                current_tokens += text_tokens
            else:
                # Can we fit a truncated version?
                remaining = budget - current_tokens
                if remaining > 100:  # Minimum useful size
                    truncated = self.truncate_text(text, remaining)
                    trimmed.append(truncated)
                break

        return trimmed

    def truncate_text(self, text: str, max_tokens: int) -> str:
        """Truncate text to max tokens"""

        tokens = self.encoding.encode(text)
        truncated_tokens = tokens[:max_tokens]
        return self.encoding.decode(truncated_tokens) + "..."

Component 5: Response Generation and Validation

Generating Context-Aware Responses

from openai import OpenAI

class InterviewResponseGenerator:
    def __init__(self):
        self.client = OpenAI()
        self.model = "gpt-4-turbo-preview"

    async def generate_followup_question(
        self,
        prompt: str,
        temperature: float = 0.7,
        max_retries: int = 3
    ) -> str:
        """Generate follow-up question with retry logic"""

        for attempt in range(max_retries):
            try:
                response = self.client.chat.completions.create(
                    model=self.model,
                    messages=[
                        {
                            "role": "system",
                            "content": "You are an expert interviewer conducting a professional, context-aware interview."
                        },
                        {
                            "role": "user",
                            "content": prompt
                        }
                    ],
                    temperature=temperature,
                    max_tokens=200,  # Follow-up questions should be concise
                    top_p=0.95
                )

                question = response.choices[0].message.content.strip()

                # Validate question quality
                if self.validate_question(question):
                    return question
                else:
                    # Retry with lower temperature
                    temperature *= 0.8

            except Exception as e:
                if attempt == max_retries - 1:
                    raise
                await asyncio.sleep(2 ** attempt)  # Exponential backoff

        return self.get_fallback_question()

    def validate_question(self, question: str) -> bool:
        """Validate generated question meets quality standards"""

        # Check length
        if len(question) < 10 or len(question) > 300:
            return False

        # Check if it's actually a question
        if not any(question.endswith(p) for p in ['?', '.', '!']):
            return False

        # Check for generic questions (avoid low-quality outputs)
        generic_phrases = [
            "tell me more",
            "anything else",
            "can you elaborate"
        ]

        question_lower = question.lower()
        if any(phrase in question_lower for phrase in generic_phrases):
            # Too generic, request more specific
            return False

        return True

Ensuring Response Relevance

Validate that generated questions are relevant to conversation:

class RelevanceValidator:
    def __init__(self):
        self.embedding_gen = EmbeddingGenerator()

    def check_relevance(
        self,
        generated_question: str,
        candidate_response: str,
        job_requirements: str,
        threshold: float = 0.6
    ) -> bool:
        """Check if generated question is relevant"""

        # Create embeddings
        question_emb = self.embedding_gen.generate_embedding(generated_question)
        context_text = f"{candidate_response} {job_requirements}"
        context_emb = self.embedding_gen.generate_embedding(context_text)

        # Calculate similarity
        similarity = cosine_similarity(
            question_emb.reshape(1, -1),
            context_emb.reshape(1, -1)
        )[0][0]

        return similarity >= threshold

Advanced RAG Techniques

1. Hierarchical Retrieval

For complex organizations with nested context:

class HierarchicalRetriever:
    def retrieve_hierarchical(
        self,
        query: str,
        company_id: str,
        department_id: str,
        role_id: str
    ) -> List[str]:
        """Retrieve context at multiple hierarchy levels"""

        # Level 1: Role-specific context (highest priority)
        role_contexts = self.retrieve_with_filter(
            query,
            {'role_id': role_id},
            top_k=3
        )

        # Level 2: Department context
        dept_contexts = self.retrieve_with_filter(
            query,
            {'department_id': department_id},
            top_k=2
        )

        # Level 3: Company-wide context
        company_contexts = self.retrieve_with_filter(
            query,
            {'company_id': company_id},
            top_k=2
        )

        # Combine with priority weighting
        all_contexts = (
            role_contexts +  # Most specific
            dept_contexts +  # Medium specificity
            company_contexts  # Most general
        )

        return self.deduplicate(all_contexts)

2. Temporal Awareness

Track when information was added/updated:

def retrieve_with_temporal_awareness(
    self,
    query: str,
    prefer_recent: bool = True,
    time_decay_factor: float = 0.1
) -> List[str]:
    """Retrieve with temporal decay for older information"""

    results = self.vector_store.query(query, top_k=20)

    if prefer_recent:
        # Apply time decay to scores
        current_time = datetime.now()

        for result in results:
            doc_age_days = (current_time - result.metadata['created_at']).days
            time_penalty = 1.0 - (doc_age_days * time_decay_factor)
            time_penalty = max(0.1, time_penalty)  # Minimum weight

            result.score *= time_penalty

        # Re-sort by adjusted scores
        results.sort(key=lambda x: x.score, reverse=True)

    return results[:5]

3. Feedback-Based Improvement

Learn from interview outcomes:

class FeedbackLearner:
    def record_question_effectiveness(
        self,
        question: str,
        retrieved_contexts: List[str],
        candidate_response_quality: float,  # 0-1 score
        eventual_hire_outcome: bool
    ):
        """Record how effective retrieval was"""

        self.feedback_db.insert({
            'question': question,
            'contexts_used': retrieved_contexts,
            'response_quality': candidate_response_quality,
            'hire_outcome': eventual_hire_outcome,
            'timestamp': datetime.now()
        })

    def optimize_retrieval_weights(self):
        """Adjust retrieval based on feedback"""

        # Analyze which types of context led to better outcomes
        feedback_data = self.feedback_db.query_recent(days=90)

        # Calculate context type effectiveness
        context_effectiveness = {}
        for feedback in feedback_data:
            for context in feedback['contexts_used']:
                context_type = context['metadata']['document_type']

                if context_type not in context_effectiveness:
                    context_effectiveness[context_type] = []

                context_effectiveness[context_type].append(
                    feedback['response_quality']
                )

        # Update retrieval weights
        for context_type, scores in context_effectiveness.items():
            avg_effectiveness = np.mean(scores)
            self.retrieval_weights[context_type] = avg_effectiveness

Performance Optimization

Caching Strategy

Cache frequently accessed contexts:

from functools import lru_cache
import redis

class CachedRetriever:
    def __init__(self):
        self.redis_client = redis.Redis(host='localhost', port=6379)
        self.cache_ttl = 3600  # 1 hour

    def retrieve_with_cache(self, query: str, job_id: str) -> List[str]:
        """Retrieve with Redis caching"""

        cache_key = f"retrieval:{job_id}:{hashlib.md5(query.encode()).hexdigest()}"

        # Check cache
        cached_result = self.redis_client.get(cache_key)
        if cached_result:
            return json.loads(cached_result)

        # Retrieve if not cached
        results = self.retrieve(query)

        # Cache results
        self.redis_client.setex(
            cache_key,
            self.cache_ttl,
            json.dumps(results)
        )

        return results

Batching and Async Operations

import asyncio

class AsyncRAGPipeline:
    async def process_batch_retrievals(
        self,
        queries: List[str]
    ) -> List[List[str]]:
        """Process multiple retrievals in parallel"""

        tasks = [
            self.retrieve_async(query)
            for query in queries
        ]

        results = await asyncio.gather(*tasks)
        return results

    async def retrieve_async(self, query: str) -> List[str]:
        """Async retrieval operation"""

        # Generate embedding
        embedding = await self.async_embedding_gen(query)

        # Query vector store
        results = await self.vector_store.query_async(embedding)

        return results

Monitoring and Evaluation

Key Metrics

class RAGMetricsTracker:
    def track_retrieval_metrics(self):
        """Track RAG system performance"""

        metrics = {
            # Retrieval Quality
            'retrieval_relevance': self.calculate_relevance(),
            'context_utilization': self.calculate_utilization(),
            'retrieval_diversity': self.calculate_diversity(),

            # Performance
            'avg_retrieval_latency': self.calculate_latency(),
            'cache_hit_rate': self.calculate_cache_hits(),
            'tokens_per_retrieval': self.calculate_token_usage(),

            # Business Impact
            'question_relevance_score': self.calculate_question_quality(),
            'interview_completion_rate': self.calculate_completion_rate(),
            'candidate_satisfaction': self.calculate_satisfaction()
        }

        return metrics

Conclusion

RAG architecture transforms AI interview systems from rigid, template-based tools into adaptive, context-aware conversational agents. Key takeaways:

1. Chunking strategy matters: Semantic chunking outperforms fixed-size
2. Hybrid retrieval wins: Combine dense and sparse for best results
3. Context management is critical: Stay within token limits intelligently
4. Validate everything: Ensure retrieved context is relevant and generated questions are high-quality
5. Monitor and improve: Use feedback to continuously optimize

The future of HR tech lies in systems that understand nuance, adapt to context, and conduct truly intelligent conversations. RAG makes this possible.

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About the Author
Ademola Balogun is the founder and CEO of 180GIG Ltd, creators of Squrrel—an AI-powered interview platform that makes hiring smarter and more equitable. With an MSc in Data Science from Birkbeck, University of London, he specializes in building practical AI solutions for real-world problems. He also created Trading Flashes ⚡, an AI-driven newsletter platform for financial markets.