Question 1

Implement cosine similarity, dot product, and Euclidean distance from scratch

Accepted Answer

Approach: Three ways to compare vectors. Cosine measures the angle (magnitude-invariant), dot product rewards both alignment and magnitude, and Euclidean is straight-line distance.

Code:
```python
import numpy as np

def cosine_similarity(a, b):
    a, b = np.asarray(a), np.asarray(b)
    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b) + 1e-8)

def dot_product(a, b):
    return float(np.dot(np.asarray(a), np.asarray(b)))

def euclidean_distance(a, b):
    a, b = np.asarray(a), np.asarray(b)
    return float(np.sqrt(np.sum((a - b) ** 2)))
```

Key points: Cosine is the default for text embeddings because it ignores vector length (document size shouldn't affect similarity). Dot product equals cosine only when vectors are normalized. Crucial fact to state: for L2-normalized vectors, cosine ranking and Euclidean ranking are identical — which is why vector DBs pre-normalize and use fast dot products.

Question 2

Implement semantic search using embeddings and cosine similarity

Accepted Answer

Approach: Embed documents once, normalize them, then rank by cosine (which reduces to a single matrix-vector dot product).

Code:
```python
import numpy as np

class SemanticSearch:
    def __init__(self, embed_fn):
        self.embed = embed_fn
        self.docs, self.vecs = [], None

def index(self, docs):
        self.docs = docs
        vecs = np.array([self.embed(d) for d in docs])
        self.vecs = vecs / np.linalg.norm(vecs, axis=1, keepdims=True)

def search(self, query, k=3):
        q = np.asarray(self.embed(query))
        q = q / np.linalg.norm(q)
        scores = self.vecs @ q  # cosine (pre-normalized)
        top = np.argsort(scores)[::-1][:k]
        return [(self.docs[i], float(scores[i])) for i in top]
```

Key points: Pre-normalizing the corpus turns cosine into a plain dot product, so search is one fast matrix multiply. This is exact (brute-force) search — fine for thousands of docs; beyond that you need an ANN index (HNSW/IVF). Embed the query with the same model as the corpus.

Reference in assignment: outcomeschool.com/blog/how-does-a-vector-database-work.

Question 3

Build a simple vector similarity search from scratch

Accepted Answer

Approach: Brute-force k-NN: compute similarity against every stored vector, then take the top-k with a partial sort.

Code:
```python
import numpy as np

def brute_force_search(query, vectors, k=5):
    query = np.asarray(query)
    vectors = np.asarray(vectors)
    sims = (vectors @ query) / (
        np.linalg.norm(vectors, axis=1) * np.linalg.norm(query) + 1e-8)
    # argpartition is O(n): get the k best without fully sorting
    top_k = np.argpartition(-sims, min(k, len(sims) - 1))[:k]
    return sorted(((int(i), float(sims[i])) for i in top_k),
                  key=lambda x: -x[1])
```

Key points: Brute force is O(n·d) — exact but linear in corpus size, so it doesn't scale to millions. Production uses approximate nearest neighbour (HNSW graphs, IVF partitioning) that trade a little recall for sub-linear search. Note the optimization: argpartition is O(n) vs argsort's O(n log n) when you only need the top-k.

Question 4

Implement a simple re-ranker for search results

Accepted Answer

Approach: A cross-encoder jointly scores each (query, document) pair for relevance — more accurate than the bi-encoder used for retrieval, but too slow to run over the whole corpus, so it re-ranks only the top candidates.

Code:
```python
from sentence_transformers import CrossEncoder

class Reranker:
    def __init__(self, model="cross-encoder/ms-marco-MiniLM-L-6-v2"):
        self.model = CrossEncoder(model)

def rerank(self, query, docs, top_k=3):
        pairs = [(query, d) for d in docs]
        scores = self.model.predict(pairs)  # joint relevance scores
        ranked = sorted(zip(docs, scores), key=lambda x: -x[1])
        return ranked[:top_k]

# Two-stage: fast retrieval -> precise rerank
candidates = semantic_search.search(query, k=20)  # bi-encoder
final = Reranker().rerank(query, [d for d, _ in candidates], top_k=3)
```

Key points: The two-stage retrieve-then-rerank pattern is standard in strong RAG: a bi-encoder retrieves ~20 cheaply (embeddings precomputed), then a cross-encoder re-scores them by reading query and doc together (richer, catches relevance a dot product misses). You can't cross-encode the whole corpus — it's per-query compute — so you only rerank t

Question 5

Write code for different text chunking strategies (fixed-size, recursive, semantic)

Accepted Answer

Approach: Three strategies of increasing sophistication: fixed windows with overlap, recursive splitting on a separator hierarchy, and semantic grouping by embedding similarity.

Code:
```python
import numpy as np

def fixed_chunk(text, size=500, overlap=50):
    chunks, start = [], 0
    while start < len(text):
        chunks.append(text[start:start + size])
        start += size - overlap  # slide with overlap
    return chunks

def recursive_chunk(text, size=500, seps=("

", "
", ". ", " ")):
    if len(text) <= size or not seps:
        return [text]
    head, *rest = seps
    chunks, cur = [], ""
    for part in text.split(head):
        if len(cur) + len(part) <= size:
            cur += part + head
        else:
            if cur: chunks.append(cur)
            chunks += (recursive_chunk(part, size, tuple(rest))
                       if len(part) > size else [part])
            cur = ""
    if cur: chunks.append(cur)
    return chunks

def semantic_chunk(sentences, embed_fn, threshold=0.75):
    embs = [np.asarray(embed_fn(s)) for s in sentences]
    chunks, cur = [], [sentences[0]]
    for i in range(1, len(sentences)):
        sim = (embs[i] @ embs[i-1]) / (

Question 6

Build a basic document parser that extracts text from PDFs and splits it into chunks

Accepted Answer

Approach: Extract text page by page, normalize whitespace, then reuse the recursive chunker — attaching source metadata for citations.

Code:
```python
import pypdf

def parse_pdf(path, size=500):
    reader = pypdf.PdfReader(path)
    chunks = []
    for page_num, page in enumerate(reader.pages):
        text = " ".join((page.extract_text() or "").split())  # normalize ws
        for c in recursive_chunk(text, size):
            chunks.append({"text": c, "page": page_num + 1, "source": path})
    return chunks
```

Key points: Always attach metadata (page, source) so retrieved chunks can cite their origin. Basic extract_text handles simple PDFs but loses tables, columns, and reading order — for real documents you'd use layout-aware parsing or a document-VLM (Surya/Chandra) that outputs structured Markdown/JSON. Whitespace normalization prevents junk tokens.

Question 7

Implement a basic RAG pipeline using an embedding model and a vector database

Accepted Answer

Approach: Three stages — index (chunk + embed), retrieve (embed query, top-k), generate (grounded prompt). A tiny in-memory store stands in for the vector DB.

Code:
```python
import numpy as np

class RAGPipeline:
    def __init__(self, embed_fn, llm_fn):
        self.embed, self.llm = embed_fn, llm_fn
        self.chunks, self.vecs = [], None

def index(self, documents):
        self.chunks = [c for d in documents for c in recursive_chunk(d)]
        vecs = np.array([self.embed(c) for c in self.chunks])
        self.vecs = vecs / np.linalg.norm(vecs, axis=1, keepdims=True)

def retrieve(self, query, k=4):
        q = np.asarray(self.embed(query)); q = q / np.linalg.norm(q)
        idx = np.argsort(self.vecs @ q)[::-1][:k]
        return [self.chunks[i] for i in idx]

def answer(self, query, k=4):
        context = "

".join(self.retrieve(query, k))
        prompt = (
            "Answer using ONLY the context below. "
            "If the answer isn't there, say you don't know.

"
            f"Context:
{context}

Question: {query}
Answer:")
        return self.llm(prompt)
```

Key points: Name the three stages explicitly. The "answer only from context / say I

Question 8

Write code to implement token counting and context window management

Accepted Answer

Approach: Count tokens with the model's real tokenizer, reserve room for the completion, and evict the oldest history turns until the request fits the budget.

Code:
```python
import tiktoken

class ContextManager:
    def __init__(self, model="gpt-4o", max_tokens=128_000, reserve=4_000):
        self.enc = tiktoken.encoding_for_model(model)
        self.budget = max_tokens - reserve  # leave room for output

def count(self, text):
        return len(self.enc.encode(text))

def fit(self, system, history, new_msg):
        used = self.count(system) + self.count(new_msg)
        kept = []
        for msg in reversed(history):  # keep the most recent
            t = self.count(msg["content"])
            if used + t > self.budget:
                break
            kept.append(msg); used += t
        return [{"role": "system", "content": system},
                *reversed(kept),
                {"role": "user", "content": new_msg}]
```

Key points: Use the model's actual tokenizer (tiktoken) — character or word counts are wrong. Always reserve tokens for the response, or the model runs out of room to answer. Evicting oldest-first preserves recent context; a smarter version summ

Question 9

Implement a prompt template system with variable substitution

Accepted Answer

Approach: Parse the template for required variables, validate that all are supplied (fail fast), then substitute safely.

Code:
```python
import re

class PromptTemplate:
    def __init__(self, template):
        self.template = template
        self.vars = set(re.findall(r"\{(\w+)\}", template))

def format(self, **kwargs):
        missing = self.vars - kwargs.keys()
        if missing:
            raise ValueError(f"Missing variables: {missing}")
        return self.template.format(**kwargs)

tmpl = PromptTemplate("Summarize this {doc_type} for a {audience}:
{content}")
print(tmpl.format(doc_type="report", audience="CEO", content="..."))
```

Key points: Extracting and validating required variables prevents silent bugs where a missing field leaves a literal {placeholder} in the prompt. Production extensions: versioning (Q10), partial application, and — importantly — escaping user-supplied content so it can't inject template syntax or instructions.

Question 10

Build a simple prompt versioning system

Accepted Answer

Approach: Hash each template's content to derive a stable version id, store versions with metadata, and retrieve by version or latest.

Code:
```python
import json, hashlib, time

class PromptRegistry:
    def __init__(self, path="prompts.json"):
        self.path = path
        try: self.store = json.load(open(path))
        except FileNotFoundError: self.store = {}

def register(self, name, template, metadata=None):
        version = hashlib.sha256(template.encode()).hexdigest()[:8]
        self.store.setdefault(name, {})[version] = {
            "template": template, "metadata": metadata or {},
            "created": time.time()}
        json.dump(self.store, open(self.path, "w"), indent=2)
        return version

def get(self, name, version=None):
        versions = self.store[name]
        if version:
            return versions[version]["template"]
        return max(versions.values(), key=lambda v: v["created"])["template"]
```

Key points: A content hash as the version id gives free deduplication and reproducibility (same prompt → same id). Store metadata like eval scores and author so you can compare versions. This is the backbone of prompt A/B testing and rollback

Question 11

Implement streaming responses for an LLM API

Accepted Answer

Approach: Consume the provider's token stream with a generator, then expose it over Server-Sent Events so the client renders tokens as they arrive.

Code:
```python
from openai import OpenAI
from fastapi import FastAPI
from fastapi.responses import StreamingResponse

client, app = OpenAI(), FastAPI()

def stream_chat(messages):
    stream = client.chat.completions.create(
        model="gpt-4o", messages=messages, stream=True)
    for chunk in stream:
        delta = chunk.choices[0].delta.content
        if delta:
            yield delta  # yield tokens as they come

@app.post("/chat")
def chat(messages: list):
    def event_stream():
        for token in stream_chat(messages):
            yield f"data: {token}

"  # SSE frame
    return StreamingResponse(event_stream(),
                              media_type="text/event-stream")
```

Key points: stream=True yields incremental deltas; wrapping them in a generator keeps memory flat. Exposing via SSE (data: ...

) lets the browser show text token-by-token. The real win is perceived latency — the user sees output at TTFT instead of waiting for the whole completion, which matters enormously for chat UX.

Reference in assignment:

Question 12

Implement a retry mechanism with exponential backoff for LLM API calls

Accepted Answer

Approach: A decorator that retries transient failures with exponentially growing, jittered delays capped at a ceiling.

Code:
```python
import time, random, functools

def retry_with_backoff(max_retries=5, base=1.0, cap=30.0):
    def decorator(fn):
        @functools.wraps(fn)
        def wrapper(*args, **kwargs):
            for attempt in range(max_retries):
                try:
                    return fn(*args, **kwargs)
                except (RateLimitError, APIError):  # transient only
                    if attempt == max_retries - 1:
                        raise
                    delay = min(cap, base * 2 ** attempt)  # exponential
                    delay += random.uniform(0, delay * 0.25)  # jitter
                    time.sleep(delay)
        return wrapper
    return decorator

@retry_with_backoff()
def call_llm(prompt):
    ...
```

Key points: Exponential backoff (base·2^attempt) spaces retries out under load; the cap prevents absurd waits; and jitter is essential — without it, many clients retry in lockstep and create a thundering herd. Only retry transient errors (429/5xx), never 4xx like invalid requests. This is table-stakes for reliable API integration.

Question 13

Write a function calling (tool use) handler for an LLM API

Accepted Answer

Approach: Declare tools with JSON schemas, let the model decide which to call, execute the real functions, feed results back, and let the model compose the final answer.

Code:
```python
import json

tools = [{
    "type": "function",
    "function": {
        "name": "get_weather",
        "description": "Get current weather for a city",
        "parameters": {
            "type": "object",
            "properties": {"city": {"type": "string"}},
            "required": ["city"]}}}]

TOOL_IMPL = {"get_weather": lambda city: f"{city}: 22C, sunny"}

def handle(messages):
    resp = client.chat.completions.create(
        model="gpt-4o", messages=messages, tools=tools)
    msg = resp.choices[0].message
    if not msg.tool_calls:
        return msg.content  # no tool needed
    messages.append(msg)
    for call in msg.tool_calls:  # run each requested tool
        args = json.loads(call.function.arguments)
        result = TOOL_IMPL[call.function.name](**args)
        messages.append({"role": "tool", "tool_call_id": call.id,
                          "content": str(result)})
    # feed observations back for the final response
    return client.chat.completions.create(
        model="gp

Question 14

Build a simple AI agent with tool use (calculator, web search)

Accepted Answer

Approach: A ReAct loop — the model alternates Thought → Action → Observation until it reaches an Answer, with a step cap to prevent infinite loops.

Code:
```python
import re

def react_agent(question, llm, tools, max_steps=5):
    prompt = f"""Use this format:
Thought: reason about what to do next
Action: tool_name[input]
Observation: (the system fills this in)
... (repeat) ...
Answer: the final answer

Tools available: {', '.join(tools)}
Question: {question}
"""
    for _ in range(max_steps):
        out = llm(prompt + "\nThought:")
        prompt += "\nThought:" + out.split("Observation:")[0]
        if "Answer:" in out:
            return out.split("Answer:")[-1].strip()
        m = re.search(r"Action:\s*(\w+)$$(.*?)$$", out)
        if not m:
            break
        tool, arg = m.group(1), m.group(2)
        obs = tools[tool](arg) if tool in tools else "Unknown tool"
        prompt += f"\nObservation: {obs}"
    return "Could not reach an answer."

tools = {"calculator": lambda x: str(eval(x)),  # use a safe parser in prod!
         "search": lambda q: web_search(q)}
```

Key points: ReAct interleaves reasoning (Thought) with acting (Action → Observation), which grounds each

Question 15

Build a multi-agent system where agents have different roles and collaborate

Accepted Answer

Approach: Define specialized agents (a role each) and orchestrate them in a pipeline, passing each output forward as context: research → write → critique → revise.

Code:
```python
class Agent:
    def __init__(self, name, role, llm):
        self.name, self.role, self.llm = name, role, llm

def run(self, task, context=""):
        prompt = f"You are {self.name}, a {self.role}.
{context}
Task: {task}"
        return self.llm(prompt)

def multi_agent_pipeline(topic, llm):
    researcher = Agent("Researcher", "gathers key facts", llm)
    writer = Agent("Writer", "writes a clear draft", llm)
    critic = Agent("Critic", "reviews and finds weaknesses", llm)

facts = researcher.run(f"List the key facts about {topic}")
    draft = writer.run("Write a short article", context=f"Facts:
{facts}")
    review = critic.run("Critique this draft", context=f"Draft:
{draft}")
    final = writer.run("Revise using the critique",
                        context=f"Draft:
{draft}

Critique:
{review}")
    return final
```

Key points: Each agent has a focused role and they collaborate through shared context passed down the pipeline. Orchestration patterns vary: sequential (shown), hiera

Question 16

Implement a conversation memory system for a chatbot (sliding window, summary, buffer)

Accepted Answer

Approach: Three strategies for retaining context: keep everything (buffer), keep the last N turns (window), or summarize old turns while keeping recent ones verbatim (summary).

Code:
```python
class BufferMemory:  # full history (until the context limit)
    def __init__(self): self.messages = []
    def add(self, role, content):
        self.messages.append({"role": role, "content": content})
    def get(self): return self.messages

class WindowMemory:  # only the last k turns
    def __init__(self, k=6): self.messages, self.k = [], k
    def add(self, role, content):
        self.messages.append({"role": role, "content": content})
        self.messages = self.messages[-self.k:]
    def get(self): return self.messages

class SummaryMemory:  # summarize old, keep recent verbatim
    def __init__(self, llm, keep=4):
        self.llm, self.keep = llm, keep
        self.summary, self.recent = "", []
    def add(self, role, content):
        self.recent.append({"role": role, "content": content})
        if len(self.recent) > self.keep:
            old = self.recent.pop(0)
            self.summary = self.llm(
                f"Update the summary.
Summary: {self.summary}
New: {old}")

Question 17

Implement a caching layer for LLM responses

Accepted Answer

Approach: Hash the full request (prompt + generation params) as the cache key, with a TTL for freshness — an exact-match cache.

Code:
```python
import hashlib, json, time

class LLMCache:
    def __init__(self, ttl=3600):
        self.store, self.ttl = {}, ttl

def _key(self, prompt, **params):
        blob = json.dumps({"prompt": prompt, **params}, sort_keys=True)
        return hashlib.sha256(blob.encode()).hexdigest()

def get_or_call(self, prompt, llm_fn, **params):
        key = self._key(prompt, **params)
        hit = self.store.get(key)
        if hit and time.time() - hit["t"] < self.ttl:
            return hit["response"]  # cache hit
        resp = llm_fn(prompt, **params)
        self.store[key] = {"response": resp, "t": time.time()}
        return resp
```

Key points: Key on the whole request — the prompt plus params like temperature and model — because those change the output. A TTL keeps responses from going stale. This is exact-match only (identical requests); for paraphrases you need semantic caching (Q18). Caveat: only cache low-temperature/deterministic-ish calls, or you'll serve one random sample forever.

Question 18

Implement semantic caching for LLM queries

Accepted Answer

Approach: Embed the query, find the nearest cached query by cosine similarity, and reuse its response if similarity clears a threshold — so paraphrases hit the cache.

Code:
```python
import numpy as np

class SemanticCache:
    def __init__(self, embed_fn, threshold=0.92):
        self.embed, self.threshold = embed_fn, threshold
        self.keys, self.vals = [], []  # query embeddings, responses

def get_or_call(self, query, llm_fn):
        q = np.asarray(self.embed(query)); q = q / np.linalg.norm(q)
        if self.keys:
            sims = np.array(self.keys) @ q
            i = int(np.argmax(sims))
            if sims[i] >= self.threshold:  # close enough to reuse
                return self.vals[i]
        resp = llm_fn(query)
        self.keys.append(q); self.vals.append(resp)
        return resp
```

Key points: Semantic caching catches "what's the capital of France?" vs "France's capital city?" — an exact cache would miss. The threshold is the critical knob: too low and you return wrong answers for merely-similar queries (dangerous false hits); too high and you rarely hit. It's a big cost/latency saver for FAQ-style, repetitive traffic; use a real ANN index for the cac

Question 19

Build an evaluation pipeline for LLM outputs using LLM-as-a-judge

Accepted Answer

Approach: A judge LLM scores each response against a rubric and returns structured JSON; run it over a dataset and aggregate.

Code:
```python
import json

JUDGE_PROMPT = """Rate the response 1-5 on each criterion.
Return ONLY JSON: {{"relevance": n, "accuracy": n, "coherence": n, "reason": "..."}}
Question: {q}
Response: {r}"""

def judge(question, response, judge_llm):
    out = judge_llm(JUDGE_PROMPT.format(q=question, r=response))
    return json.loads(out)

def run_eval(dataset, system_fn, judge_llm):
    results = []
    for item in dataset:
        resp = system_fn(item["question"])
        results.append({"q": item["question"],
                         **judge(item["question"], resp, judge_llm)})
    keys = ["relevance", "accuracy", "coherence"]
    avg = {k: sum(r[k] for r in results) / len(results) for k in keys}
    return avg, results
```

Key points: A structured rubric + JSON output makes scores parseable and comparable across runs. Be ready to discuss judge bias — position, verbosity, and self-preference — and mitigations: constrain with explicit rubrics, use a strong judge distinct from the model under test, and calibrate against human labels. This pipeline is the e

Question 20

Write code to detect and handle hallucinations in LLM outputs

Accepted Answer

Approach: Faithfulness check — verify the answer's claims are supported by the retrieved context (NLI-style via an LLM judge), and fall back to "I don't know" when they aren't.

Code:
```python
def check_grounding(answer, context, judge_llm):
    prompt = f"""Is the ANSWER fully supported by the CONTEXT?
Reply ONLY 'SUPPORTED' or 'UNSUPPORTED: <the unsupported claim>'.
CONTEXT: {context}
ANSWER: {answer}"""
    return judge_llm(prompt)

def safe_answer(query, rag, judge_llm):
    context = "
".join(rag.retrieve(query))
    answer = rag.answer(query)
    verdict = check_grounding(answer, context, judge_llm)
    if verdict.startswith("UNSUPPORTED"):
        return "I don't have enough reliable information to answer that."
    return answer
```

Key points: For RAG, hallucination is best framed as faithfulness: is every claim backed by the source? Complementary detection signals worth mentioning: self-consistency (sample N answers, disagreement flags fabrication) and low token-probability/high uncertainty. Detection is probabilistic — combine methods and fall back gracefully. The real defense remains grounding + citations, not a perfect detector.

Coding and Practical Implementation

Coding and Practical Implementation